Call mapping systems and methods using variance algorithm (va) and/or distribution compensation

ABSTRACT

Method, system and program product, comprising obtaining agent performance data; ranking, agents based the agent performance data; dividing agents into agent performance ranges; partitioning callers based on criteria into a set of partitions; determining for each partition an outcome value for a first agent performance range and a second agent performance range; calculating for the partitions a respective outcome value difference indicator based on the outcome value for the first agent performance range and the outcome value for the second agent performance range for the partition; matching a respective agent to a respective caller in one of the partitions, based on the outcome value difference indicators for the partitions.

CROSS REFERENCE TO RELATED APPLICATION

This application is continuation application of U.S. Ser. No. 13/843,724filed Mar. 15, 2013, which claims priority from Provisional U.S.Application 61/615,788 filed Mar. 26, 2012, and from Provisional U.S.Application 61/615,779 filed Mar. 26, 2012, and from Provisional U.S.Application 61/615,772 filed Mar. 26, 2012, all of which areincorporated herein by reference in their entirety as if fully set forthherein.

BACKGROUND

The present invention relates generally to the field of routing phonecalls and other telecommunications in a contact center system.

The typical contact center consists of a number of human agents, witheach assigned to a telecommunication device, such as a phone or acomputer for conducting email or Internet chat sessions, that isconnected to a central switch. Using these devices, the agents aregenerally used to provide sales, customer service, or technical supportto the customers or prospective customers of a contact center or acontact center's clients.

Typically, a contact center or client will advertise to its customers,prospective customers, or other third parties a number of differentcontact numbers or addresses for a particular service, such as forbilling questions or for technical support. The customers, prospectivecustomers, or third parties seeking a particular service will then usethis contact information, and the incoming caller will be routed at oneor more routing points to a human agent at a contact center who canprovide the appropriate service. Contact centers that respond to suchincoming contacts are typically referred to as “inbound contactcenters.”

Similarly, a contact center can make outgoing contacts to current orprospective customers or third parties. Such contacts may be made toencourage sales of a product, provide technical support or billinginformation, survey consumer preferences, or to assist in collectingdebts. Contact centers that make such outgoing contacts are referred toas “outbound contact centers.”

In both inbound contact centers and outbound contact centers, theindividuals (such as customers, prospective customers, surveyparticipants, or other third parties) that interact with contact centeragents using a telecommunication device are referred to in thisapplication as a “caller.” The individuals employed by the contactcenter to interact with callers are referred to in this application asan “agent.”

Conventionally, a contact center operation includes a switch system thatconnects callers to agents. In an inbound contact center, these switchesroute incoming callers to a particular agent in a contact center, or, ifmultiple contact centers are deployed, to a particular contact centerfor further routing. In an outbound contact center employing telephonedevices, dialers are typically employed in addition to a switch system.The dialer is used to automatically dial a phone number from a list ofphone numbers, and to determine whether a live caller has been reachedfrom the phone number called (as opposed to obtaining no answer, a busysignal, an error message, or an answering machine). When the dialerobtains a live caller, the switch system routes the caller to aparticular agent in the contact center.

Routing technologies have accordingly been developed to optimize thecaller experience. For example, U.S. Pat. No. 7,236,584 describes atelephone system for equalizing caller waiting times across multipletelephone switches, regardless of the general variations in performancethat may exist among those switches. Contact routing in an inboundcontact center, however, is a process that is generally structured toconnect callers to agents that have been idle for the longest period oftime. In the case of an inbound caller where only one agent may beavailable, that agent is generally selected for the caller withoutfurther analysis. In another example, if there are eight agents at acontact center, and seven are occupied with contacts, the switch willgenerally route the inbound caller to the one agent that is available.If all eight agents are occupied with contacts, the switch willtypically put the contact on hold and then route it to the next agentthat becomes available. More generally, the contact center will set up aqueue of incoming callers and preferentially route the longest-waitingcallers to the agents that become available over time. Such a pattern ofrouting contacts to either the first available agent or thelongest-waiting agent is referred to as “round-robin” contact routing.In round robin contact routing, eventual matches and connections betweena caller and an agent are essentially random.

Some attempts have been made to improve upon these standard yetessentially random processes for connecting a caller to an agent. Forexample, U.S. Pat. No. 7,209,549 describes a telephone routing systemwherein an incoming caller's language preference is collected and usedto route their telephone call to a particular contact center or agentthat can provide service in that language. In this manner, languagepreference is the primary driver of matching and connecting a caller toan agent, although once such a preference has been made, callers arealmost always routed in “round-robin” fashion.

BRIEF SUMMARY OF THE EMBODIMENTS

Embodiments of a method are disclosed a method is disclosed comprising:obtaining, by one or more computers, agent performance data for a set ofagents; ranking, by the one or more computers, the agents based at leastin part on the agent performance data; dividing, by the one or morecomputers, the agents into agent performance ranges based at least inpart on the ranking step; partitioning, by the one or more computers,callers in a set of callers based on one or more criteria into a set ofpartitions; determining for each of the partitions, by the one or morecomputers, an outcome value at least for a first one of the agentperformance ranges and for a second one of the agent performance ranges;calculating, by the one or more computers, for each of the partitions anoutcome value difference indicator based at least in part on adifference between the outcome value for the first agent performancerange and the outcome value for the second agent performance range; andmatching, by the one or more computers, a respective agent withrespective performance data to a respective caller in one of thepartitions, based at least in part on the outcome value differenceindicators for the partitions.

In embodiments, the determining an outcome value step may comprisedetermining a mean outcome value for a particular outcome for the agentperformance range.

In embodiments, the partition may be based at least in part on one ormore selected from the group of demographic data, area code, zip code,NPANXX, VTN, geographic area, 800 number, and transfer number.

In embodiments, the outcome value comprises one or more selected fromthe group of sale, number of items sold per call, and revenue per callcoupled with handle time.

In embodiments, the matching step may be based at least in part on arule to assign a higher performing agent to a caller from one of thepartitions where the outcome value difference indicator is higherrelative to other of the outcome value difference indicators, whereinthe higher performing agent is determined based at least in part on theagent performance data for the respective agent relative to the agentperformance data for other of the agents.

In embodiments, the matching step may further comprise the steps:calculating, by the one or more computers, a ranking or percentile forthe partitions by Δ from a high Δ to a low Δ based at least in part on afirst number of calls; calculating a ranking or percentile of the agentperformances, by the one or more computers, for the respective agents inthe set of agents based at least in part on the respective agentperformance data and a second number of calls; and matching, by the oneor more computers, the respective agent to the respective caller basedat least in part on a rule to minimize a difference between thepartition percentile of the outcome value difference indicator for thepartition of the respective caller and the performance percentile of therespective agent.

In embodiments, the matching step may be performed based at least inpart using a multi-data element pattern matching algorithm in apair-wise fashion to obtain a score or other indicator for each ofmultiple caller-agent pairs from a set of agents and a set of callers.

In embodiments, the method may further comprise correcting, by the oneor more computers, for time effects by subtracting from agentperformance data an epoch average from a desired outcome data sample,wherein the epoch average comprises an average of one selected from thegroup of hour-of-day desired outcome data, day-of-week desired outcomedata, and hour-of-week desired outcome data, from an epoch in which arespective call occurs, to obtain a new target outcome variable.

In embodiments, the method may further comprise correcting, by the oneor more computers, for time effects by dividing agent performance databy an epoch average from a desired outcome data sample, wherein theepoch average comprises an average of one selected from the group ofhour-of-day desired outcome data, day-of-week desired outcome data, andhour-of-week desired outcome data from an epoch in which a respectivecall occurs, to obtain a new target outcome variable.

In embodiments, the method may further comprise correcting, by the oneor more computers, for time effects by forming an outer product bycombining hour-of-day desired outcome data, day-of-week desired outcomedata, and hour-of-week desired outcome data from an epoch in which arespective call occurs to obtain a time effects factor; and calculating,by the one or more computers, the agent performance using the timeeffects factor in a Bayesian Mean Regression calculation of the agentperformance.

In embodiments, the method may further comprise: switching, by the oneor more computers, between the outcome value difference indicatormatching algorithm and a second matching algorithm that is differentfrom the outcome value difference indicator matching algorithm, based onone or more criteria; and when there has been switching to the secondmatching algorithm, then performing, by the one or more computers,performing the second matching algorithm to match a respective one ofthe agents with a respective one of the callers.

In embodiments, the method may further comprise: obtaining results data,by the one or more computers, using each of the outcome value differenceindicator matching algorithm and the second matching algorithm in thematching step; determining a switch-over point in the distribution foragent performance and/or the distribution for caller propensity wherebetter results are obtained from one of the algorithms relative to theother of the algorithms; and using, by the one or more computers, thisswitch-over point to switch between the algorithms.

In embodiments, the method may further comprise: switching, by the oneor more computers, between the outcome value difference indicatormatching algorithm and a second matching algorithm configured to match arespective agent with a respective ranking or percentile to a caller inone of the partitions with a closest ranking or percentile, based on oneor more criteria; and when there has been switching to the secondmatching algorithm, then performing, by the one or more computers,performing the second matching algorithm to match a respective one ofthe agents with a respective one of the callers.

In embodiments, a system is disclosed, comprising: one or morecomputers, configured with program code, that when executed, perform thesteps: obtaining, by the one or more computers, agent performance datafor a set of agents; ranking, by the one or more computers, the agentsbased at least in part on the agent performance data; dividing, by theone or more computers, the agents into agent performance ranges based atleast in part on the ranking step; partitioning, by the one or morecomputers, callers in a set of callers based on one or more criteriainto a set of partitions; determining for each of the partitions, by theone or more computers, an outcome value at least for a first one of theagent performance ranges and for a second one of the agent performanceranges; calculating, by the one or more computers, for each of thepartitions an outcome value difference indicator based at least in parton a difference between the outcome value for the first agentperformance range and the outcome value for the second agent performancerange; and matching, by the one or more computers, a respective agentwith respective performance data to a respective caller in one of thepartitions, based at least in part on the outcome value differenceindicators for the partitions.

In embodiments, a program product is disclosed, comprising: anon-transitory computer-readable medium configured with program code,that when executed by one or more computers, causes the performance ofthe steps: obtaining, by the one or more computers, agent performancedata for a set of agents; ranking, by the one or more computers, theagents based at least in part on the agent performance data; dividing,by the one or more computers, the agents into agent performance rangesbased at least in part on the ranking step; partitioning, by the one ormore computers, callers in a set of callers based on one or morecriteria into a set of partitions; determining for each of thepartitions, by the one or more computers, an outcome value at least fora first one of the agent performance ranges and for a second one of theagent performance ranges; calculating, by the one or more computers, foreach of the partitions an outcome value difference indicator based atleast in part on a difference between the outcome value for the firstagent performance range and the outcome value for the second agentperformance range; and matching, by the one or more computers, arespective agent with respective performance data to a respective callerin one of the partitions, based at least in part on the outcome valuedifference indicators for the partitions.

The exemplary processes may further include or be supplemented by apattern matching algorithm. For example, a pattern matching algorithmmay use demographic data of the agents and/or callers to predict achance of one or more outcome variables based on historical caller-agentpairings. The comparison via a pattern matching algorithm may becombined with the matching via corresponding agent performance andpropensity of callers to determine a caller-agent match and routingdecision.

Agent data may include agent grades or rankings, agent historical data,agent demographic data, agent psychographic data, and otherbusiness-relevant data about the agent (individually or collectivelyreferred to in this application as “agent data”). Agent and callerdemographic data can comprise any of: gender, race, age, education,accent, income, nationality, ethnicity, area code, zip code, maritalstatus, job status, and credit score. Agent and caller psychographicdata can comprise any of introversion, sociability, desire for financialsuccess, and film and television preferences. It is further noted thatcertain data, such an area code, may provide statistical data regardingprobable income level, education level, ethnicity, religion, and so on,of a caller which may be used by the exemplary process to determine apropensity of caller for a particular outcome variable, for example.

The examples can be applied broadly to different processes for matchingcallers and agents. For instance, exemplary processes or models mayinclude conventional queue routing, performance based matching (e.g.,ranking a set of agents based on performance and preferentially matchingcallers to the agents based on a performance ranking or score), anadaptive pattern matching algorithm or computer model for matchingcallers to agents (e.g., comparing caller data associated with a callerto agent data associated with a set of agents), affinity data matching,combinations thereof, and so on. The methods may therefore operate tooutput scores or rankings of the callers, agents, and/or caller-agentpairs for a desired optimization of an outcome variable (e.g., foroptimizing cost, revenue, customer satisfaction, and so on). In oneexample, different models may be used for matching callers to agents andcombined in some fashion with the exemplary multiplier processes, e.g.,linearly weighted and combined for different performance outcomevariables (e.g., cost, revenue, customer satisfaction, and so on).

According to another aspect, computer-readable storage media andapparatuses are provided for mapping and routing callers to agentsaccording to the various processes described herein. Many of thetechniques described here may be implemented in hardware, firmware,software, or combinations thereof. In one example, the techniques areimplemented in computer programs executing on programmable computersthat each includes a processor, a storage medium readable by theprocessor (including volatile and nonvolatile memory and/or storageelements), and suitable input and output devices. Program code isapplied to data entered using an input device to perform the functionsdescribed and to generate output information. The output information isapplied to one or more output devices. Moreover, each program ispreferably implemented in a high level procedural or object-orientedprogramming language to communicate with a computer system. However, theprograms can be implemented in assembly or machine language, if desired.In any case, the language may be a compiled or interpreted language.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram reflecting the general setup of a contact center andits operation.

FIG. 2 illustrates an exemplary routing system having a routing enginefor routing callers based on performance and/or pattern matchingalgorithms.

FIG. 3 illustrates an exemplary routing system having a mapping enginefor matching callers to agents based on a probability multiplier processalone or in combination with one or more additional matching processes.

FIGS. 4A and 4B illustrate an exemplary probability matching process andrandom matching process, respectively.

FIGS. 5A and 5B illustrate exemplary probability matching processes formatching a caller to an agent.

FIGS. 6A and 6B illustrate exemplary three-dimensional plots of agentperformance, client propensity, and the probability of a sale forparticular caller-agent pairings.

FIG. 7 illustrates an exemplary probability matching process or computermodel for matching callers to agents based on probabilities of outcomevariables.

FIG. 8 illustrates an exemplary probability matching process or computermodel for matching callers to agents based on probabilities of outcomevariables.

FIG. 9 illustrates an exemplary probability matching process or computermodel for matching callers to agents based on probabilities of outcomevariables.

FIG. 10 illustrates a typical computing system that may be employed toimplement some or all processing functionality in certain embodiments ofthe invention.

FIG. 11 illustrates an exemplary probability matching process orcomputer model for matching callers to agents using a variancealgorithm.

FIG. 12 illustrates an exemplary probability matching process orcomputer model for matching callers to agents using distributioncompensation.

FIG. 13 is an exemplary graph of agent utilization with nagents=100,mu=20, Kappa=1 and no edge correction.

FIG. 14 is an exemplary graph of agent utilization with nagents=100,mu=20, Kappa=1 and a topological edge correction (Alternating CircleMethod).

FIG. 15 is an exemplary graph of agent utilization with nagents=100,mu=20, Kappa=1 and the Adaptive Shift Method of edge correction.

FIG. 16 is an exemplary graph of agent utilization with nagents=100,mu=10, Kappa=1 and the Static Shift Method v5 of edge correction.

FIG. 17 is an exemplary graph of agent utilization with nagents=100,mu=20, Kappa=1 and the Static Shift Method v6 of edge correction.

FIG. 18 is an exemplary graph of agent utilization with nagents=100,mu=40, Kappa=1 and the Static Shift Method v6 of edge correction.

FIG. 19 is an exemplary graph of agent utilization with nagents=100,mu=10, Kappa=1.4 and the Adaptive Shift Method of edge correction.

FIG. 20 is an exemplary graph of agent utilization with nagents=100,mu=10, Kappa=1.4 and no edge correction.

FIG. 21 is an exemplary graph of agent utilization with nagents=100,mu=10, Kappa=1.4 and the Static Shift Method v5 of edge correction.

FIG. 22 is an exemplary graph of agent utilization with nagents=100,mu=10, Kappa=1.4 and the Static Shift Method v6 of edge correction.

FIG. 23 is an exemplary graph of agent utilization with nagents=100,mu=10, Kappa=1.4 and the Alternating Circle Method of edge correction.

FIG. 24 is an exemplary graph illustrating the time effects of meanrevenue per call vs. days in a week in a month.

FIG. 25 is an exemplary graph illustrating the time effects of meanrevenue per call vs. days in one week.

FIG. 26 is an exemplary graph illustrating the time effects of meanrevenue per call vs. hours of the day.

FIG. 27 is an exemplary graph illustrating normalized power spectraldensity vs. frequency (1/hour).

DETAILED DESCRIPTION OF EMBODIMENTS

Exemplary call mapping and routing systems and methods are described,for example, in U.S. patent application Ser. No. 12/267,471, entitled“Routing Callers to Agents Based on Time Effect Data,” filed on Nov. 7,2008; Ser. No. 12/490,949, entitled “Probability Multiplier Process forCall Center Routing,” filed on Jun. 24, 2009; and Ser. No. 12/266,418,entitled, “Pooling Callers for Matching to Agents Based on PatternMatching Algorithms,” filed on Nov. 6, 2008, U.S. patent applicationSer. No. 12/051,251 filed on Jan. 28, 2008, and U.S. patent applicationSer. No. 12/267,471 filed on Jan. 28, 2010, and provisional applicationno. 61/084,201 filed Jul. 28, 2008, all of which are incorporated hereinby reference in their entirety.

The following description is presented to enable a person of ordinaryskill in the art to make and use the invention, and is provided in thecontext of particular applications and their requirements. Variousmodifications to the embodiments will be readily apparent to thoseskilled in the art, and the generic principles defined herein may beapplied to other embodiments and applications without departing from thespirit and scope of the invention. Moreover, in the followingdescription, numerous details are set forth for the purpose ofexplanation. However, one of ordinary skill in the art will realize thatthe invention might be practiced without the use of these specificdetails. In other instances, well-known structures and devices are shownin block diagram form in order not to obscure the description of theinvention with unnecessary detail. Thus, the present invention is notintended to be limited to the embodiments shown, but is to be accordedthe widest scope consistent with the principles and features disclosedherein.

While the invention is described in terms of particular examples andillustrative figures, those of ordinary skill in the art will recognizethat the invention is not limited to the examples or figures described.Those skilled in the art will recognize that the operations of thevarious embodiments may be implemented using hardware, software,firmware, or combinations thereof: as appropriate. For example, someprocesses can be carried out using processors or other digital circuitryunder the control of software, firmware, or hard-wired logic. (The term“logic” herein refers to fixed hardware, programmable logic and/or anappropriate combination thereof, as would be recognized by one skilledin the art to carry out the recited functions.) Software and firmwarecan be stored on computer-readable storage media. Some other processescan be implemented using analog circuitry, as is well known to one ofordinary skill in the art. Additionally, memory or other storage, aswell as communication components, may be employed in embodiments of theinvention.

According to certain aspects of the present invention, systems. andmethods are provided for matching callers to agents within a callrouting center based on similar rankings or relative probabilities for adesired outcome variable. In one example, an exemplary probabilitymultiplier process include matching the best agents to the best callers,the worst agents to worst callers, and so on, based on the probabilityof a desired outcome variable. For instance, agents may be scored orranked based on performance for an outcome variable such as sales,customer satisfaction, cost, or the like. Additionally, callers can bescored or ranked for an outcome variable such as propensity orstatistical chance to purchase (which may be based on available callerdata, e.g., phone number, area code, zip code, demographic data, type ofphone used, historical data, and so on). Callers and agents can then bematched according to their respective rank or percentile rank; forexample, the highest ranking agent matched with the highest rankingcaller, the second highest ranked agent with the second highest caller,and so on.

The exemplary probability multiplier process takes advantage of theinherent geometric relationship of multiplying the differentprobabilities, for example, a 30% sales rate agent with a 30% buyingcustomer (giving you a total chance of 9%) as opposed to matching a 20%or 10% sales rate agent with that same customer (resulting in a 6% or 3%chance). When used across all agents and callers, the process results ina higher overall predicted chance of a particular outcome variable, suchas sales, than a random matching process.

In one example, in addition to using relative ranks of agents andcallers to match callers to agents, a pattern matching algorithm usingagent and/or caller demographic data may be used. For instance, agentsand callers may be matched based on demographic data via a patternmatching algorithm, such as an adaptive correlation algorithm. Thecaller-agent matches from the probability multiplier algorithm andpattern matching algorithms can be combined, e.g., linearly combinedwith or without weightings, to determine a final match for routing acaller to an agent.

Initially, exemplary call routing systems and methods are described formatching callers to available agents. This description is followed byexemplary systems and methods for ranking or ordering callers and agentsbased on an outcome variable, e.g., sales, customer satisfactions, orthe like, and matching agents to callers based on the relative rankings.For instance, matching the highest ranking agent for a particularoutcome variable with the highest ranking caller for a particularoutcome variable, matching the lowest ranking agent with the lowestranking caller, and so on.

FIG. 1 is a diagram reflecting the general setup of a typical contactcenter operation 100. The network cloud 101 reflects a specific orregional telecommunications network designed to receive incoming callersor to support contacts made to outgoing callers. The network cloud 101can comprise a single contact address, such as a telephone number oremail address, or multiple contact addresses. The central router 102reflects contact routing hardware and software designed to help routecontacts among call centers 103. The central router 102 may not beneeded where there is only a single contact center deployed. Wheremultiple contact centers are deployed, more routers may be needed toroute contacts to another router for a specific contact center 103. Atthe contact center level 103, a contact center router 104 will route acontact to an agent 105 with an individual telephone or othertelecommunications equipment 105. Typically, there are multiple agents105 at a contact center 103.

FIG. 2 illustrates an exemplary contact center routing system 200 (whichmay be included with contact center router 104 of FIG. 1). Broadlyspeaking, routing system 200 is operable to match callers and agentsbased, at least in part and in one example, on a probability multiplierprocess based on agent performance and caller propensity (e.g.,statistical chance or likelihood) for a particular outcome variable.Routing system 200 may further be operable to match callers based onpattern matching algorithms using caller data and/or agent data alone orin combination with the probability multiplier process. Routing system200 may include a communication server 202 and a routing engine 204 forreceiving and matching callers to agents (referred to at times as“mapping” callers to agents).

In one example, and as described in greater detail below, routing engine204 is operable to determine or retrieve performance data for availableagents and caller propensity for an outcome variable from callers onhold. The performance data and caller propensity data may be convertedto percentile ranks for each and used to match callers to agents basedon the closest match of percentile ranks, respectively, therebyresulting in high performing agents matched to callers with a highpropensity to purchase, for example.

Additionally, in some examples, routing engine 204 may additionallyinclude pattern matching algorithms and/or computer models, which mayadapt over time based on the performance or outcomes of previouscaller-agent matches. The additional pattern matching algorithms may becombined in various fashions with a probability multiplier process todetermine a routing decision. In one example, a pattern matchingalgorithm may include a neural network based adaptive pattern matchingengine as is known in the art; for example, a resilient backpropagation(RProp) algorithm, as described by M. Riedmiller, H. Braun: “A DirectAdaptive Method for Faster backpropagation Learning: The RPROPAlgorithm,” Proc. of the IEEE Intl. Cont'. on Neural Networks 1993,which is incorporated by reference herein in its entirety. Various otherexemplary agent performance and pattern matching algorithms and computermodel systems and processes which may be included with contact routingsystem and/or routing engine 204 are described, for example, in U.S.Ser. No. 12/021,251, filed Jan. 28, 2008, and U.S. Serial No. U.S.patent application Ser. No. 12/202,091, filed Aug. 29, 2008, both ofwhich are hereby incorporated by reference in their entirety. Of course,it will be recognized that other performance based or pattern matchingalgorithms and methods may be used alone or in combination with thosedescribed here.

Routing system 200 may further include other components such ascollector 206 for collecting caller data of incoming callers, dataregarding caller-agent pairs, outcomes of caller-agent pairs, agent dataof agents, historical performance data of agents, and the like. Further,routing system 200 may include a reporting engine 208 for generatingreports of performance and operation of routing system 200. Variousother servers, components, and functionality are possible for inclusionwith routing system 200. Further, although shown as a single hardwaredevice, it will be appreciated that various components may be locatedremotely from each other (e.g., communication server 202 and routingengine 204 need not be included with a common hardware/server system orincluded at a common location). Additionally, various other componentsand functionality may be included with routing system 200, but have beenomitted here for clarity.

FIG. 3 illustrates further detail of exemplary routing engine 204.Routing engine 204 includes a main mapping engine 304, which may includeone or more mapping engines therein for use alone or in combination withother mapping engines. In some examples, routing engine 204 may routecallers based solely or in part on performance data associated withagents and caller data associated with the propensity or chances of aparticular outcome variable. In other examples, routing engine 204 mayfurther make routing decisions based solely or in part on comparingvarious caller data and agent data, which may include, e.g., performancebased data, demographic data, psychographic data, type of phone/phonenumber, BTN-data, and other business-relevant data. Additionally,affinity databases (not shown) may be used and such information receivedby routing engine 204 and/or mapping engine 304 for making orinfluencing routing decisions. Database 312 may include local or remotedatabases, third party services, and so on (additionally, mapping engine304 may receive agent data from database 314 if applicable for theparticular mapping process).

In one example, relative agent performance may be determined by rankingor scoring a set of agents based on performance for a particular outcomevariable (such as revenue generation, cost, customer satisfaction,combinations thereof, and the like). Further, the relative agentperformance may be converted to a relative percentile ranking.Processing engine 320-1, for example, may determine or receive relativeagent performance data for one or more outcome variables. Additionally,processing engine 320-1 may receive or determine a propensity of acaller for a particular outcome variable (such as propensity topurchase, length of call, to be satisfied, combinations thereof, and thelike). The propensity of a caller may be determined from availablecaller data. The relative performance data of the agents and propensitydata of the callers may then be used to match a caller and an agentbased on corresponding ranking. In some examples, the performance andpropensity data is converted to relative percentile rankings for thecallers and agents, and matching callers and agents based on the closestrespective relative percentiles.

Processing engine 320-2, in one example, includes one or more patternmatching algorithms, which operate to compare available caller data witha caller to agent data associated a set of agents and determine asuitability score of each caller-agent pair. Processing engine 320-2 mayreceive caller data and agent data from various databases (e.g., 312 and314) and output caller-agent pair scores or a ranking of caller-agentpairs, for example. The pattern matching algorithm may include acorrelation algorithm such as a neural network algorithm, geneticalgorithm, or other adaptive algorithm(s).

Additionally, a processing engine may include one or more affinitymatching algorithms, which operate to receive affinity data associatedwith the callers and/or agents. Affinity data and/or affinity matchingalgorithms may be used alone or in combination with other processes ormodels discussed herein.

Routing engine 204 may further include selection logic (not shown) forselecting and/or weighting one or more of the plurality of processingengines 320-1 and 320-2 for mapping a caller to an agent. For example,selection logic may include rules for determining the type and amount ofcaller data that is known or available and selecting an appropriateprocessing engine 320-1,320-2, etc., or combinations thereof. Selectionlogic may be included in whole or in part with routing engine 204,mapping engine 304, or remotely to both.

Further, as indicated in FIG. 3 at 350, call history data (including,e.g., caller-agent pair data and outcomes with respect to cost, revenue,customer satisfaction, and so on) may be used to retrain or modifyprocessing engines 320-1 and 320-2. For instance, the agent performancedata may be updated periodically (e.g., daily) based on historicaloutcomes to re-rank the agents. Further, historical informationregarding callers may be used to update information regarding callerpropensities for particular outcome variables.

In some examples, routing engine 204 or main mapping engine 304 mayfurther include a conventional queue based routing processes, which maystore or access hold or idle times of callers and agents, and operate tomap callers to agents based on a hold time or queue order of the callers(and/or agents). Further, various function or time limits may be appliedto callers on hold to ensure that callers are not held too long awaitingan agent. For instance, if a caller's time limit (whether based on apredetermined value or function related to the caller) is exceeded thecaller can be routed to the next available agent.

Additionally, an interface may be presented to a user allowing foradjustment of various aspects of the exemplary systems and methods, forexample, allowing adjustments of the number of different models,degrees, and types of caller data. Further, an interface may allow forthe adjustment of the particular models used for different degrees ortypes, for example, adjusting an optimization or weighting of aparticular model, changing a model for a particular degree or type ofcaller data, and so on. The interface may include a slider or selectorfor adjusting different factors in real-time or at a predetermined time.Additionally, the interface may allow a user to turn certain methods onand off, and may display an estimated effect of changes. For instance,an interface may display the probable change in one or more of cost,revenue generation, or customer satisfaction by changing aspects of therouting system. Various estimation methods and algorithms for estimatingoutcome variables are described, for example, in copending U.S.provisional Patent application Ser. No. 61/084,201, filed on Jul. 28,2008, and which is incorporated herein by reference in its entirety. Inone example, the estimate includes evaluating a past time period of thesame (or similar) set of agents and constructing a distribution ofagent/caller pairs. Using each pair, an expected success rate can becomputed via the performance based matching, pattern matching algorithm,etc., and applied to current information to estimate current performance(e.g., with respect to one or more of sales, cost, customersatisfaction, etc.). Accordingly, taking historical call data and agentinformation the system can compute estimates of changing the balance orweighting of the processing methods. It is noted that a comparable time(e.g., time of day, day of the week etc.) for the historical informationmay be important as performance will likely vary with time.

FIG. 4A schematically illustrates an exemplary probability multiplierprocess for matching callers and agents and FIG. 4B illustrates a randommatching process (e.g., queue based or the like). These illustrativeexamples assume that there are five agents and five callers to bematched. The agents can be ranked based on performance of a desiredoutcome variable. For instance, the agents may be scored and orderedbased on a statistical chance of completing a sale based on historicalsales rate data. Additionally, the callers can be scored and rankedbased on a desired outcome variable, for example, on a propensity orlikelihood to purchase products or services. The callers may be rankedand ordered based on known or available caller data including, forexample, demographic data, zip codes, area codes, type of phone used,and so on, which are used to determine a statistical or historicalchance of the caller making a purchase.

The agents and callers are then matched to each other based on theranking, where the highest ranked agent is matched to the highest rankedcaller, the second highest ranked agent matched to the second highestranked caller, and so on. Matching the best to the best and worst to theworst results in an increase product of the matched pairs compared torandomly matching callers to agents as shown in FIG. 4B. For instance,using illustrative sales rates for agents A1-A5 (e.g., based on pastagent performance) and the chance of callers C1-C5 making a purchase(e.g., based on caller data such as demographic data, caller data, andso on), the product of the matches shown in FIG. 4A is as follows:

(0.09*0.21)+(0.07*0.12)+(0.06*0.04)+(0.05*0.03)+(0.02*0.02)0.0316

In contrast, for a random matching, as illustrated in FIG. 4B and usingthe same percentages, the product is as follows:

(0.09*0.12)+(0.07*0.02)+(0.06*0.21)+(0.05*0.03)+(0.02*0.04)0.0271

Accordingly, matching the highest ranking agent with the highest rankingcaller and the worst ranking agent with the worst ranking callerincreases the overall product, and thus chances of optimizing thedesired outcome variable (e.g., sales).

FIG. 5A schematically illustrates an exemplary process for matchingcallers on hold to an agent that becomes free. In this example, allagents A1-A5 on duty or all that might become free within a reasonablehold time of callers C1-C5 are scored or ranked as previously described.Additionally, callers C1-C5 are scored or ranked as previouslydescribed. As an agent, e.g., agent A2 becomes free the processdetermines that caller C2 is the same (or similar) rank as agent A2 andcaller C2 is matched thereto. The remaining callers on hold may then bere-ranked for matching when the next agent becomes free. Additionally,as new callers are placed on hold the callers can be reranked in areal-time fashion. The exemplary process operates in a similar fashionfor multiple free agents and a caller becomes free (both for inbound andoutbound call centers).

It will be recognized that in most instances the number of agents andcallers will not be equal. Accordingly, the callers (and/or agents) canbe ranked and converted to relative percentile rankings for the callers;for example, a normalized ranking or setting the highest ranked calleras the 100^(th) percentile and the lowest ranked caller as the 0^(th)percentile. The agents may be similarly converted to relative percentilerankings. As an agent becomes free, the agent may be matched to thecaller having the closest relative percentile rank to the agent'srelative percentile rank. In other examples, as an agent becomes freethe agent can be compared to the ranking of at least a portion ofcallers on hold to compute Z-scores for each agent-caller pair. Thehighest Z-score may correspond to the smallest difference in relativepercentile rankings. Further, as noted herein, the Zscores may be usedto combine the matching with other algorithms such as pattern matchingalgorithms, which may also output a Z-score.

FIG. 5B schematically illustrates exemplary methods for matching callersand agents when an agent becomes free and multiple callers are on hold.In this example, the callers (and in some examples the agents) aregrouped in sub-groups of performance. For instance, a range of callerperformance may be divided into multiple sub-groups and callers bucketedwithin each group. The top 20% of callers by performance might begrouped together as C1₁-C1_(N) as illustrated, followed by the next 20%,and so on. As an agent becomes free, e.g., A2, a caller from anappropriate sub-group is matched to the caller, in this example fromC2₁-C2_(N). Within the sub-group the caller may be chosen by a queueorder, best-match, a pattern matching algorithm, or the like. Theappropriate sub-group from which to route a caller may be determinedbased on the agent ranking or score, for example.

In one example, suppose it is desired to optimize a call centerperformance for Outcome variable O. O can include one or more of salesrate, customer satisfaction, first call resolution, or other variables.Suppose further that at some time there are N_(A) agents logged in andN_(c) callers in queue. Suppose that agents have performances ingenerating O of

A _(i) ^(o) i=1, . . . ,N _(A)

and callers, partitioned by some property P, have a propensity to O of

C _(i) ^(o) i=1, . . . ,N _(C)

For example, in the case where O is sales rate and P is caller areacode, A^(O) is each agent's sales rate and C^(O) is the sales rate forcallers in a particular area code. Calculating the percentile rankedagent performances (with respect to the set of logged in agents) and thepercentile ranked caller propensities (with respect to the set ofcallers in queue at some instant of time) as follows:

A _(Pi) ^(O) =pr(A _(i) ^(O) ,A ^(O))(i=1, . . . ,N _(A))

C _(Pi) ^(O) =pr(C _(i) ^(O) ,C ^(O))(i=1, . . . ,N _(C))

where pr(a, B) is the percentile rank function which returns the rank ofvalue a with respect to the set of values B scaled into the range[0,100].

Suppose that all the agents are on calls when the k'th agent becomesavailable. Then to determine which caller in the queue they should beconnected to, compute the difference between the percentile ranks of thenewly free k'th agent and those of the callers in queue:

D _(j) =A _(Pk) ^(O) −C _(Pj) ^(O))j=1, . . . ,N _(C))

The value of j indexing the minimum element of the set {D_(J)} gives themember of the queue to connect to the k'th agent. A Z-score can also bederived from the D_(j). This has the advantages that the highest valueagent-caller pairing is the best fit of the set and that the output fromthis algorithm can be combined with Z-score outputs from otheralgorithms since they have the same scale.

Z _(j)=(T _(j)−μ)/σ

where μ and σ and the mean and standard deviation of T which is givenby:

T _(j)=Min(D _(j))−D _(j)

It will be recognized by those of skill in the art that the aboveexample and algorithm described for the case of two variables is notrestricted to the case of two variables, but can be extended in anobvious way to the case of more than two variables which aremonotonically related to the desired outcome. Furthermore the increasein call center performance can be shown to increase with more variables,as will be understood and contemplated by those of ordinary skill in theart.

FIG. 6A illustrates an exemplary three-dimensional plot of agentperformance versus caller propensity along the x-axis and y-axis and theprobability of a sale along the z-axis. In this example, agentperformance and caller propensity are defined as linear functions of xand y. For instance, without loss of generality, xε[0,1] and yε[0,1],such that the agent propensity is:

a=ca+ma x

where “ca” and “ma” represent the intercept and slope for the agentperformance linear function (note that in certain instances herein,multiple letters represent a single variable e.g., “ca” and “ma” areeach single variables, and are offset by other letters to indicate amultiplication operator). Similarly for the caller propensity:

c=cc+mc y

where “cc” and “mc” represent the intercept and slope for the callerpropensity linear function. The multiplicative model probability of salep of the product of a and c is:

p=a c

The average height d of a surface of the probability of a sale, which isgraphically illustrated in FIG. 6A as surface 600, can be computed asfollows:

$d = {{\int_{0}^{1}{\int_{0}^{1}{p\ {x}\ {y}}}} = {\frac{1}{4}\left( {{2\mspace{14mu} {ca}} + {ma}} \right)\left( {{2\mspace{14mu} {cc}} + {mc}} \right)}}$

Determining the average height n of the diagonal of the surface height“pdiag” (a single variable), which corresponds to multiplying similarpercentile ranking agents against callers is as follows:

pdiag = (ca + ma λ)(cc + mc λ);$n = {{\int_{0}^{1}{{pdiag}\ {\lambda}}} = {{{ca}\mspace{20mu} {cc}} + \frac{{ca}\mspace{14mu} {mc}}{2} + \frac{{cc}\mspace{14mu} {ma}}{2} + \frac{{ma}\mspace{14mu} {mc}}{3}}}$

where λ parametrises the diagonal function so one can integrate down theline.

The boost b, or the potential increase in performance or sales rateaccording to the probability matching by corresponding rates, asillustrated by the diagonal shaded band 602 on surface 600, can becomputed as follows:

$b = {{{n/d} - 1} = {\frac{4\left( {{{ca}\mspace{14mu} {cc}} + \frac{{ca}\mspace{14mu} {mc}}{2} + \frac{{cc}\mspace{14mu} {ma}}{2} + \frac{{ma}\mspace{14mu} {mc}}{3}} \right)}{\left( {{2\mspace{14mu} {ca}} + {ma}} \right)\left( {{2\mspace{14mu} {cc}} + {mc}} \right)} - 1}}$

where the theoretical maximum boost of matching according toprobability, for this illustrative example, is ⅓. Accordingly, matchingcallers to agents on or near diagonal shaded band 602 increases theprobability of sales.

FIG. 6B illustrates an exemplary analysis and plot for normaldistribution of callers and agents for agent performance and callerpropensity (as opposed to a uniform distribution of performance).Assuming the same definitions of agent performance and callerpropensity, a two dimensional Gaussian function can be used to representthe distribution of call frequencies across the agents' performance andcallers' propensities:

${g\; 2{d\left( {x,y,{x\; 0},{y\; 0},\sigma} \right)}} = ^{- \frac{{({x - {x\; 0}})}^{2} - {({y - {y\; 0}})}^{2}}{2\; \sigma^{2}}}$

The sales rate can then be represented by a function of a and c, where Aand σ give the amplitude and standard deviation of the Gaussiancomponent respectively. Assuming that the Gaussian is centered at {0.5,0.5}, the probability can be written as:

$\begin{matrix}{p = {{ac}\left( {1 + {{Ag}\; 2{d\left( {x,y,\frac{1}{2},\frac{1}{2},\sigma} \right)}}} \right)}} \\{= {\left( {{ca} + \max} \right)\left( {{cc} + {mcy}} \right)\left( {A\; ^{\frac{{- {({x - \frac{1}{2}})}^{2}} - {({y - \frac{1}{2}})}^{2}}{2\sigma^{2}} + 1}} \right)}}\end{matrix}$

The diagonal sales rate, d, can then be determined directly from thesales rate as:

${d\left( {x,{ca},{ma},{cc},{mc},\sigma,x} \right)} = {\left( {A\; ^{\frac{{({x - \frac{1}{2}})}^{2}}{\sigma^{2}} + 1}} \right)\left( {{ca} + \max} \right)\left( {{cc} + {mcx}} \right)}$

Integrating this with respect to x over [0,1] gives the sales rate forcall agent pairs occurring on the diagonal giving:

$\frac{1}{12}{^{- \frac{1}{4\sigma^{2}}}\left( {{^{\frac{1}{4\sigma^{2}}}\left( {{3\sqrt{\pi}A\; \sigma \; {{erf}\left( \frac{1}{2\sigma} \right)}\left( {{\left( {{2\mspace{14mu} {ca}} + {ma}} \right)\left( {{2\mspace{14mu} {cc}} + {mc}} \right)} + {2\mspace{14mu} {ma}\mspace{14mu} {mc}\; \sigma^{2}}} \right)} + {6\mspace{14mu} {{ca}\left( {{2\mspace{14mu} {cc}} + {mc}} \right)}} + {6\mspace{14mu} {cc}\mspace{14mu} {ma}} + {4\mspace{14mu} {ma}\mspace{14mu} {mc}}} \right)} - {6\mspace{14mu} A\mspace{14mu} {ma}\mspace{14mu} {mc}\; \sigma^{2}}} \right)}$

The sales rate for random client agent pairings can be computed as:totalsalesrate[ca_, ma_, cc_, mc_, σ_, A_]=∫₀∫₀ salesrate[x, y, ca, ma,cc, mc, σ, A]d×dy which expands to:

$\frac{1}{4}\left( {{2\mspace{14mu} {ca}} + {ma}} \right)\left( {{2\mspace{14mu} {cc}} + {mc}} \right)\left( {{2\pi \; A\; \sigma^{2}{{erf}\left( \frac{1}{2\sqrt{2}\sigma} \right)}^{2}} + 1} \right)$

and the boost of the algorithm can be computed as follows:

norrmalboost[ca _(—) ,ma _(—) ,cc _(—) ,mc_,σ_(—),A_]=diagintegral[ca,ma,cc,mc,σ,A]/totalsalesrate[ca,ma,cc,mc,σ,A]−1

which results in a boost in sales of:

$\frac{^{- \frac{1}{4\sigma^{2}}}\left( {{^{\frac{1}{4\sigma^{2}}}\begin{pmatrix}{{3\sqrt{\pi}A\; \sigma \; {{erf}\left( \frac{1}{2\sigma} \right)}\left( {{\left( {{2\mspace{14mu} {ca}} + {ma}} \right)\left( {{2\mspace{20mu} {cc}} + {mc}} \right)} + {2\mspace{14mu} {ma}\mspace{14mu} {mc}\; \sigma^{2}}} \right)} +} \\{{6\mspace{14mu} {ca}\left( {{2\mspace{14mu} {cc}} + {mc}} \right)} + {6\mspace{14mu} {cc}\mspace{14mu} {ma}} + {4\mspace{14mu} {ma}\mspace{14mu} {mc}}}\end{pmatrix}} - {6\mspace{14mu} A\mspace{14mu} {ma}\mspace{14mu} {mc}\; \sigma^{2}}} \right)}{3\left( {{2\mspace{14mu} {ca}} + {ma}} \right)\left( {{2\mspace{14mu} {cc}} + {mc}} \right)\left( {{2\pi \; A\; \sigma^{2}{{erf}\left( \frac{1}{2\sqrt{2}\sigma} \right)}^{2}} + 1} \right)} - 1$

Accordingly, and similar to the normal distribution of FIG. 6A, matchingcallers to agents on or near diagonal shaded band 602 increases theprobability of sales. Of course, it will be understood that theexemplary functions, assumptions, and distributions of callerperformance and agent propensity are illustrative, and will vary basedon, e.g., historical data, feedback, and the like. Further, additionalconsiderations and variables may be incorporated into the generalprocesses. Note also that while referring to Sales as the variable tooptimize in the above example, the same procedure can be applied toother variables or combinations thereof which are to be optimized suchas call handle time (e.g. cost), or first call resolution, or manyothers.

To the extent that there is a discrepancy in any of the foregoingequations as compared to application Ser. No. 12/490,949, the equationsof application Ser. No. 12/490,949 are the correct equations and takeprecedence. FIG. 7 illustrates an exemplary process for matching callersto agents within a call routing center. In this example, agents areranked based on a performance characteristic associated with an outcomevariable such as sales or customer satisfaction at 702. In some examplesagent performance may be determined for each agent from historical dataover a period of time. In other examples, the method may merely retrieveor receive agent performance data or agent ranking for the agents.

In one example, agents are graded on an optimal interaction, such asincreasing revenue, decreasing costs, or increasing customersatisfaction. Grading can be accomplished by collating the performanceof a contact center agent over a period of time on their ability toachieve an optimal interaction, such as a period of at least 10 days.However, the period of time can be as short as the immediately priorcontact to a period extending as long as the agent's first interactionwith a caller. Moreover, the method of grading agents can be as simpleas ranking each agent on a scale of 1 to N for a particular optimalinteraction, with N being the total number of agents. The method ofgrading can also comprise determining the average contact handle time ofeach agent to grade the agents on cost, determining the total salesrevenue or number of sales generated by each agent to grade the agentson sales, or conducting customer surveys at the end of contacts withcallers to grade the agents on customer satisfaction. The foregoing,however, are only examples of how agents may be graded; many othermethods may be used.

Callers are ranked or scored based on an outcome variable based oncaller data at 704. Callers may be ranked or scored based on a predictedchance of a particular outcome based on known or available caller data.The amount and type of caller data may vary for each caller but can beused to determine a statistical chance for a particular outcome based onhistorical outcomes. For instance, the only data known for a callermight be an area code, which is associated with a particular propensityto purchase based on past interactions with callers from the particulararea code. In some examples, there may be no data associated with thecaller, in which case an average propensity or statistical chance forthe particular outcome when no data is known may be used.

Callers and agents are then matched based on their respective rankingsat 706. For example, matching the better agents to the better callersand so on as described. Additionally, to account for an uneven number ofcallers and agents either or both rankings can be adjusted or normalizedand the callers and agents routed based on a closest match. Forinstance, the rank of an agent may be divided by the number of agents,and similarly for the callers, and the callers matched to agents basedon a closest match (or within a certain range). The process may thenroute, or cause the routing, of the caller to the agent at 708. In otherexamples, the process may pass the match on to other apparatuses orprocesses that may use the match in other processes or use to weightwith other routing processes.

FIG. 8 illustrates another exemplary process for matching callers toagents within a call routing center. In this example, agents are rankedbased on a performance characteristic associated with an outcomevariable such as sales or customer satisfaction and converted to arelative percentile ranking at 702. For example, the raw performancevalues of the agents can be converted into a relative percentileranking; for example, a 9% sales rate might be converted to an 85%performance ranking. In other examples, the raw performance values canbe converted to a standardized score or Z-score.

Callers are ranked or scored based on an outcome variable based oncaller data and converted to a relative percentile ranking at 804.Similar to that of the agents, raw predicted values for the callers canbe converted into a percentile ranking; for example, a 20% propensity orlikelihood to purchase might be converted to a 92% percentile rankingamongst callers. In other examples, the raw values can be converted to astandardized score or Z-score.

Callers and agents are then matched based on their respective relativepercentile rankings at 806. For example, the relative percentile rankingof a caller can be compared to relative percentile ranking of agents andthe caller matched to the closest agent available. In examples where anagent becomes free and multiple callers are on hold the agent may bematched to the closest matching caller. In other examples, a caller maybe held for a predetermined time for the best matching agent to becomefree and then matched and routed to the closest matching agent.

It will be recognized that various other fashions of ranking callers andagents, and matching callers to agents based on their respectiverankings, are contemplated. For example, generally speaking, theexemplary processes result in higher ranking callers being routed tohigher ranking agents and lower ranking callers being routed to lowerranking agents.

FIG. 9 illustrates another exemplary process for matching callers toagents within a call routing center based on both a probabilitymultiplier process and a pattern matching algorithm. The processincludes determining relative agent performance of a set of agents foran outcome variable at 902 and determining relative caller propensity ofa set of callers for the outcome variable at 904. The relative agentperformance and relative caller propensity may further be normalized orconverted to relative percentile rankings at 906.

A portion or all of available agent data and caller data may be passedthrough a pattern matching algorithm at 908. In one example, thematching algorithm includes an adaptive pattern matching algorithm suchas a neural network algorithm that is trained on previous caller-agentpairing outcomes.

The matching algorithm may include comparing demographic data associatedwith the caller and/or agent for each caller-agent pair and computing asuitability score or ranking of caller-agent pairs for a desired outcomevariable (or weighting of outcome variables). Further, a Z-score can bedetermined for each caller-agent pair and outcome variable(s); forinstance, co-pending U.S. patent application Ser. No. 12/202,091, filedAug. 29, 2009, describes exemplary processes for computing Z-scores forcaller-agent pairs and is incorporated by reference herein in itsentirety.

Exemplary pattern matching algorithms and computer models can include acorrelation algorithm, such as a neural network algorithm or a geneticalgorithm. In one example, a resilient backpropagation (RProp) algorithmmay be used, as described by M. Riedmiller, H. Braun: “A Direct AdaptiveMethod for Faster backpropagation Learning: The RPROP Algorithm,” Proc.of the IEEE Intl. Conf. on Neural Networks 1993, which is incorporatedby reference herein in its entirety. To generally train or otherwiserefine the algorithm, actual contact results (as measured for an optimalinteraction) are compared against the actual agent and caller data foreach contact that occurred. The pattern matching algorithm can thenlearn, or improve its learning of, how matching certain callers withcertain agents will change the chance of an optimal interaction. In thismanner, the pattern matching algorithm can then be used to predict thechance of an optimal interaction in the context of matching a callerwith a particular set of caller data, with an agent of a particular setof agent data. Preferably, the pattern matching algorithm isperiodically refined as more actual data on caller interactions becomesavailable to it, such as periodically training the algorithm every nightafter a contact center has finished operating for the day.

The pattern matching algorithm can be used to create a computer modelreflecting the predicted chances of an optimal interaction for eachagent and caller matching. For example, the computer model may includethe predicted chances for a set of optimal interactions for every agentthat is logged in to the contact center as matched against everyavailable caller. Alternatively, the computer model can comprise subsetsof these, or sets containing the aforementioned sets. For example,instead of matching every agent logged into the contact center withevery available caller, exemplary methods and systems can match everyavailable agent with every available caller, or even a narrower subsetof agents or callers. The computer model can also be further refined tocomprise a suitability score for each matching of an agent and a caller.

In other examples, exemplary models or methods may utilize affinity dataassociated with callers and/or agents. For example, affinity data mayrelate to an individual caller's contact outcomes (referred to in thisapplication as “caller affinity data”), independent of theirdemographic, psychographic, or other business-relevant information. Suchcaller affinity data can include the caller's purchase history, contacttime history, or customer satisfaction history. These histories can begeneral, such as the caller's general history for purchasing products,average contact time with an agent, or average customer satisfactionratings. These histories can also be agent specific, such as thecaller's purchase, contact time, or customer satisfaction history whenconnected to a particular agent.

As an example, a certain caller may be identified by their calleraffinity data as one highly likely to make a purchase, because in thelast several instances in which the caller was contacted, the callerelected to purchase a product or service. This purchase history can thenbe used to appropriately refine matches such that the caller ispreferentially matched with an agent deemed suitable for the caller toincrease the chances of an optimal interaction. Using this embodiment, acontact center could preferentially match the caller with an agent whodoes not have a high grade for generating revenue or who would nototherwise be an acceptable match, because the chance of a sale is stilllikely given the caller's past purchase behavior. This strategy formatching would leave available other agents who could have otherwisebeen occupied with a contact interaction with the caller. Alternatively,the contact center may instead seek to guarantee that the caller ismatched with an agent with a high grade for generating revenue;irrespective of what the matches generated using caller data and agentdemographic or psychographic data may indicate.

In one example, affinity data and an affinity database developed by thedescribed examples may be one in which a caller's contact outcomes aretracked across the various agent data. Such an analysis might indicate,for example, that the caller is most likely to be satisfied with acontact if they are matched to an agent of similar gender, race, age, oreven with a specific agent. Using this example, the method couldpreferentially match a caller with a specific agent or type of agentthat is known from the caller affinity data to have generated anacceptable optimal interaction.

Affinity databases can provide particularly actionable information abouta caller when commercial, client, or publicly-available database sourcesmay lack information about the caller. This database development canalso be used to further enhance contact routing and agent-to-callermatching even in the event that there is available data on the caller,as it may drive the conclusion that the individual caller's contactoutcomes may vary from what the commercial databases might imply. As anexample, if an exemplary method was to rely solely on commercialdatabases in order to match a caller and agent, it may predict that thecaller would be best matched to an agent of the same gender to achieveoptimal customer satisfaction. However, by including affinity databaseinformation developed from prior interactions with the caller, anexemplary method might more accurately predict that the caller would bebest matched to an agent of the opposite gender to achieve optimalcustomer satisfaction.

Callers can then be matched to agents at 910 based on a comparison ofrelative rankings determined in 906 and the pattern matching algorithmat 908. For instance, outcomes of both processes may be combined, e.g.,via a linear or non-linear combination, to determine the best matchingcaller-agent pair.

The selection or mapping of a caller to an agent may then be passed to arouting engine or router for causing the caller to be routed to theagent at 912. The routing engine or router may be local or remote to asystem that maps the caller to the agent. It is noted that additionalactions may be performed, the described actions do not need to occur inthe order in which they are stated, and some acts may be performed inparallel.

Variance Algorithm Mapping:

A further embodiment is next described for call mapping based at leastin part on a variance algorithm. The example and description below isgenerally described in terms of agent performance (AP), however, ananalogous problem exists for estimating caller propensities, forinstance to purchase, and the same methodology applies. Accordingly, toavoid repeating terms, examples will be expressed in terms of agentperformances (AP) with it being understood that this could refer tocaller propensity (CP) equally based on various partitions.

An exemplary call mapping and routing system can utilize three differentmathematical types of target data. Binomial, for instance conversionrate (CR) that is sale/no sale, multinomial, e.g., a number of revenuegeneration per unit (RGU's) sold per call, and continuous, e.g., revenueper call and handle time. All the techniques described here apply to all3 kinds of data though they need differences in the mathematicaltechniques used, particularly in the Bayesian Mean Regression (BMR)case,as will be recognized by those of ordinary skill in the art. Again, toavoid cluttering the argument with repetition, term CR is usedthroughout but this should be understood to be a stand in for binomial,multinomial, or continuous data.

Systems and methods are provided herein that can be used to improve oroptimize the mapping and routing of callers to agents in a contactcenter, where the mapping and routing of callers may use performancebased routing techniques. In one aspect of the present invention, amethod and system attempts to map or assign high performing agents tocallers belonging to groups wherein agent performance makes a largedifference to call outcomes and map or assign poor performing agents tocallers in groups where agent performance makes relatively less of adifference. In one example, the method and system assumes that apartitioning of the callers has been made, e.g., {P}, which may bedefined in many ways. Possible partitions may be based at least in parton caller demographics, caller NPA (aka area code) or NPANXX (first 6digits on phone number), or vector directory number (VDN), or geographicarea, or 800 number, or transfer number, to name a few. By whatevermethod, the callers are partitioned into sets for each of which variousstatistics for the partition may be calculated.

An exemplary process may be carried out as follows:

1. Calculate the best estimate of Agent Performance (AP's), for instanceby a Bayesian mean regression method or the like. Divide the set ofagents by their AP values into a top half set of best performing agents{T} and a bottom half set of agents {B}. This may be done by splittingthe agents at the median AP and assigning those with AP greater than themedian to {T} and the remainder to {B}. Note that in embodiments, theagents may be divided in more than two splits or groups based onperformance, or based on one or more other parameters relating todemographics or personality.

2. Partition, by one or more computers, callers in a set of callersbased on one or more criteria into a set of partitions. For eachpartition, P_(i), calculate the mean Conversion Rate (CR) for calls inP_(i) taken by agents in {T} and perform the same calculation for agentsin {B}. Then calculate a difference between these quantities, Δ.Partitions with a large A are those in which agents make a lot ofdifference, that is, high performing agents obtain a much higher CR thanlow performing agents. Conversely, partitions with small or zero Δ's areones where agents make little or no difference.

3. Calculate a ranking or percentile for the partitions by Δ from thehighest Δ to the lowest Δ, e.g., a partition in the 97^(th) percentilehas 97% of the calls in partitions with a lower Δ.

4. Calculate a percentile of the agent performances (AP's). (Thesepercentilings are by numbers of calls in each group, caller partition oragent, and assure that there are approximately equal numbers of eachavailable at matching time, thus avoiding biasing the pool of agents orcallers). Thus, in embodiments an agent with a 97^(th) percentile has 97percent of the calls going to agents with lower performance ranking.

5. In embodiments, calls may be assigned to agent—caller pairs where thedifference between the percentile Δ for the partition of the caller andthe percentile of the agent's AP is minimized. Thus, in embodiments,calls from partitions with a higher Δ will be matched to higherperforming agents and calls with a lower Δ will be matched to lowerperforming agents.

Referring to FIG. 11, implementations of embodiments are disclosed.Block 1100 represents an operation of obtaining, by one or morecomputers, agent performance data for a set of agents. In embodiments,the agent performance data may comprise one selected from the group ofsale/no sale, number of items sold per call, and revenue per callcoupled with handle time. In embodiments, this performance data may beobtained by accessing a database containing such performance data

Block 1110 represents an operation of ranking, by the one or morecomputers, the agents based at least in part on the agent performancedata.

Block 1120 represents an operation of dividing, by the one or morecomputers, the agents into agent performance ranges based at least inpart on the ranking step.

Block 1130 represents an operation of partitioning, by the one or morecomputers, callers in a set of callers based on one or more criteriainto a set of partitions. In embodiments, the partition may be based atleast in part on or more selected from the group of demographic data,area code, zip code, NPANXX, VTN, geographic area, 800 number, andtransfer number, to name a few.

Block 1140 represents an operation of determining for each of thepartitions, by the one or more computers, an outcome value at least fora first one of the agent performance ranges and an outcome value for asecond one of the agent performance ranges. In embodiments, the outcomevalue comprises one or more selected from the group of sale, number ofitems sold per call, and revenue per call coupled with handle time. Inembodiments, the determining an outcome value step may comprisedetermining a mean outcome value for a particular outcome for the agentperformance range.

Block 1150 represents an operation of calculating, by the one or morecomputers, for each of the partitions an outcome value differenceindicator based at least in part on a difference between the outcomevalue for the first agent performance range and the outcome value forthe second agent performance range.

Block 1160 represents an operation of matching, by the one or morecomputers, a respective agent with respective performance data to arespective caller in one of the partitions, based at least in part onthe outcome value difference indicators for the partitions.

In embodiments, the matching step may be based at least in part on arule to assign a higher performing agent to a caller from one of thepartitions where the outcome value difference indicator is higherrelative to other of the outcome value difference indicators, whereinthe higher performing agent is determined based at least in part on theagent performance data for the respective agent relative to the agentperformance data for other of the agents. In embodiments, one or morethreshold values may be used for the outcome value difference indicatorsand different ranges of agent performances may be associated with theserespective thresholds. In embodiments, rankings or percentiles may beused, and agents with rankings or percentiles that are closest to theranking or percentile of the outcome value difference indicator for thecaller's partition may be matched.

In embodiments, the matching step further comprises the steps ofcalculating, by the one or more computers, a ranking or percentile forthe partitions by Δ from a high Δ to a low Δ based at least in part on afirst number of number of calls; and calculating, a ranking orpercentile of the agent performances (AP's), by the one or morecomputers, for the respective agents in the set of agents based at leastin part on the respective agent performance data and a second number ofcalls. In embodiments, these percentilings or rankings are by numbers ofcalls in each group, caller partition or agent, and assure that thereare approximately equal numbers of each available at matching time, thusavoiding biasing the pool of agents or callers. In embodiments, thematching step may further comprise matching, by the one or morecomputers, the respective agent to the respective caller based at leastin part on a rule to minimize a difference between the partitionpercentile or ranking of the outcome value difference indicator for thepartition of the respective caller and the performance percentile orranking of the respective agent.

In embodiments, a mix a performance data may be used. In embodiments,higher dimensional matching may be performed, wherein the matching stepis performed for a conversion rate or a conversion rate delta, and ahandle time, or a customer satisfaction score.

In embodiments, a mix of matching algorithms may be used. For example,the one or more computers may be configured with program code toperform, when executed, the steps of: switching, by the one or morecomputers, between the outcome value difference indicator matchingalgorithm and a second matching algorithm that is different from theoutcome value difference indicator matching algorithm, based on one ormore criteria. In this example, when there has been switching to thesecond matching algorithm, then performing, by the one or morecomputers, the second matching algorithm to match a respective one ofthe agents with a respective one of the callers. In embodiments, thesecond matching algorithm may be a pattern matching algorithm, or aclosest percentile or ranking matching of agents and callers, or othermatching type algorithm. In embodiments, the system may switch to arandom matching algorithm or a pure queue based algorithm to demonstratethe relative effectiveness of the use of the outcome value differenceindicator matching algorithm as compared to a random or queue-basedmatching.

In embodiments, the one or more computers are configured with programcode to perform, when executed, the steps of: obtaining results data, bythe one or more computers, using each of the outcome value differenceindicator matching algorithm and the second matching algorithm in thematching step; obtaining or receiving, by the one or more computers, aswitch-over point in the distribution for agent performance and/or thedistribution for caller propensity where better results are obtainedfrom one of the algorithms relative to the other of the algorithms; andusing, by the one or more computers, this switch-over point to switchbetween the algorithms. The switch point may be determined empirically,by reviewing the performance data of the algorithm models based on thetwo different types of performance data. This review may be performedmanually or may be determined automatically, on a periodic or aperiodicbasis or at runtime, based on a comparison of performance results and adetermination that a predetermined difference threshold between theperformance results for the respective algorithms has been exceeded. Inembodiments, the use of a mix of performance data may also be used forhandle time, or customer satisfaction, or another parameter.

In embodiments, a pure conversion rate ranking or percentile may be usedfor low agent conversion rates (e.g., poor sales). Then the system mayswitch to using rankings or percentiles for delta. Accordingly, inembodiments, the one or more computers may be configured with programcode to perform, when executed, the steps of: switching, by the one ormore computers, between the outcome value difference indicator matchingalgorithm and a second matching algorithm configured to match arespective agent with a respective ranking or percentile to a caller inone of the partitions with a closest ranking or percentile, based on oneor more criteria; and when there has been switching to the secondmatching algorithm, then performing, by the one or more computers,performing the second matching algorithm to match a respective one ofthe agents with a respective one of the callers.

Time Effects Compensation:

A further time effects embodiment for call mapping is disclosed thatcompensates for time effects which may stand on its own or beincorporated into other embodiments herein. As noted previously, theexample and description below is generally described in terms of agentperformance (AP), however, an analogous problem exists for estimatingcaller propensities, for instance to purchase, and the same methodologyapplies. Accordingly, to avoid repeating terms, examples will beexpressed in terms of agent performances (AP) with it being understoodthat this could refer to caller propensity (CP) equally.

Conversion Rates (CR's) in call centers typically vary over time. Forexample, there are periodic changes, such as variations in CR throughthe day, day of week effects, and longer cycles. There may also besecular changes. Causes for the latter include marketing campaigns,seasonal effects such as spending in the holiday season, and the stateof the economy.

These effects, if not compensated for, can impact accurate calculationof both Agent Performance (AP) and CP. For example, if a particular callcenter had a higher CR for nighttime calls than during the daytime, thenagents who only worked a night shift would appear to have a higherperformance than those that worked during the day. But this higherperformance would be an artifact and if uncorrected, would result in aninaccurate AP. See FIGS. 12-14.

Systems and methods are provided herein that can be used to improve oroptimize the mapping and routing of callers to agents in a contactcenter, where the mapping and routing of callers may use performancebased routing techniques. In one aspect of the present invention,systems and methods are provided for mapping callers to agents based onagent and/or caller data, wherein the agent and/or caller data includesor is based on timing effects (e.g., time data or information thataffects one or more of the agent and/or caller data used to determinethe mapping of callers to agents. For instance, agent data and callerdata utilized by a pattern matching algorithm may include time effectdata associated with performance, probable performance, or outputvariables as a function of one or more of time of day, day of week, timeof month, time of year, to name a few. The pattern matching algorithmmay operate to compare caller data associated with each caller to agentdata associated with each agent to determine an optimal matching of acaller to an agent, and include an analysis of time effects on theperformance of agents or probable outcomes of the particular matchingwhen performing the matching algorithm.

In embodiments, secular trends in CR may be detected by smoothing of thecall center data, for instance with a one day or one week moving windowaverage. Periodic effects may be detected both by a power spectralanalysis techniques such as the FFT or Lomb Scargle Periodogram (WilliamH. Press and George B. Rybicki Fast Algorithm for Spectral Analysis ofUnevenly Sampled Data Astrophysical Journal, 338:277-280, March 1989,which is incorporated herein by reference), day of week CR, hour of dayCR, across many epochs. For example, a data sample of 3 months of datamay be used, and a mean and standard errors (SE) of the mean may becalculated for all the Mondays, Tuesdays, etc.), and by simply computingmeans and standard errors of the mean.

FIGS. 24-27 show evidence of hour of day, day of week and other cycletiming effects and exemplify the above. FIG. 24 illustrates mean revenuevariations over a month. FIG. 25 illustrates mean revenue variationsover days of the week. FIG. 26 illustrates mean revenue variations overhours of the day. FIG. 27 illustrates a normalized power spectraldensity vs. frequency (1 hour).

Exemplary Methods of Correcting for Secular Trends are provided. Dailymeans of CR from a period, typically some months long, are fitted eitherwith a linear least squares regression or a locally weighted leastsquares smoother like lowess (see e.g., Cleveland, W. S. (1979) Robustlocally weighted regression and smoothing scatterplots. J. Amer.Statist. Assoc. 74, 829-836, which is incorporated herein by reference)or loess. Corrected CR data can then be obtained by dividing the rawdaily time series by the fitted values.

Exemplary Methods of Correcting for Periodic Variations are alsoprovided. Epoch averages of CR (conversion rate) by hour of day (HOD),or by day of week (DOW) or by hour of week (HOW), which combines HOD andDOW effects in one variable, may be computed from a suitably long datasample.

Below are 3 exemplary methods of correction:

1. Additive: A new target variable is defined as the raw target variableless the epoch average for the epoch in which the call occurs. In thismethod and the methods to follow, the term “target variable” meanswhatever variable the call mapping system is optimizing. In the case ofconversion rate, it would be {O, I}, in the case of revenue generatingunits (RGU), the number of RGUs per call, or in the revenue case therevenue per call.

2. Multiplicative: A new target variable is defined as the raw targetvariable divided by the epoch average for the epoch in which the calloccurs.

3. Microskill Method: The relevant time effect, HOD, DOW, or HOW iscombined with the skills (or VDNs) used in the AP calculation by formingthe outer product. For example, if are 2 skills, A and B, and one wereconsidering the DOW correction only, one could form 2×7=14 newmicroskills comprising A & Monday, A & Tuesday, . . . , A & Sunday, B &Monday, B & Tuesday, . . . , B & Sunday. These 14 microskills would thenbe used as the factor in the BMR calculation of agent performance. Forexample, when time effects are applied at the skill level or the VDNlevel, and there are 10 skill for an agent that are measured, then atime effects adjustment for the respective agent may be made for eachhour of the 24 day for each of the 10 skills, e.g., 10×24=240 timeeffects adjustments made to the performance data, i.e., a microskilladjustment.

When corrected data with a new target variable has been computed, AP andCP and all other mapping calculations may be done using the correctedtarget variable.

In embodiments, a method for correcting, by the one or more computers,for time effects comprises subtracting from a target outcome variable anepoch average from a desired outcome data sample, wherein the epochaverage comprises an average of one selected from the group ofhour-of-day, day-of-week, and hour-of-week desired outcome data from anepoch in which a respective call occurs, to obtain a new target outcomevariable.

In embodiments, a method for correcting, by the one or more computers,for time effects comprises dividing a target outcome variable by anepoch average from a desired outcome data sample, wherein the epochaverage comprises an average of one selected from the group ofhour-of-day, day-of-week, and hour-of-week desired outcome data from anepoch in which a respective call occurs, to obtain a new target outcomevariable

In embodiments, a method for correcting, by the one or more computers,for time effects comprises forming an outer product by combiningselected epoch data, such as hour-of-day, day-of-week, and hour-of-weekdesired outcome data from an epoch in which a respective call occurs, toobtain a factor to be used in the Bayesian Mean Regression (BMR)calculation of agent performance.

Distribution Compensation:

In embodiments, matching algorithms may be used to match agents tocallers where a given number reflecting a parameter of an agent ismatched to a caller with the closest number reflecting a differentparameter of the caller. For example, in embodiments, the number for theagent may be a rank or a percentile reflecting the performance of therespective agent relative to other agents in a set of agents. Likewise,the number for the caller may be a rank or percentile of the callerrelative to other callers in a queue or other grouping, such as forcaller propensity for something. In embodiments, the rank or percentilemay be for a partition (e.g., demographic, zip code, area code, etc.) ofthe callers based at least in part on whether or not the level ofperformance of the agent makes a difference in the outcome of the call.

When such algorithms are used, it has been discovered that, using theexample of agent performance percentiles, the matching algorithm tendsto assign/cluster callers to agents in the middle percentiles in thedistribution of performances at the expense of agents at the ends of thedistribution (e.g., the worst agents and the best agents). Inembodiments, to compensate for this effect, an edge compensationalgorithm is provided to increase the likelihood that agents at theedges of the performance distribution are utilized more. In oneembodiment, the edge compensation algorithm takes the agents that arefree at runtime, and rescales the respective agent performances forthese runtime available agents to provide more space/margin at the edgesof the performance distribution, e.g., weighting the performance numbersfor the worst agents to increase their respective performance levelsand/or to decrease the respective performance levels of the best agents.The amount of the margin of weighting is based on the number of agentsthat are free at runtime. For example, if there are n agents working ofwhich k are free to take the call, we linearly rescale the n agentpercentiles so that the bottom agent has percentile 100/(2*k) and thetop agent has percentile 100*(1−(2/k)). In another embodiment, the edgecompensation algorithm takes the callers in a queue or other groupingand rescales the respective caller propensity rankings or percentiles,to provide more space/margin at the edges of the distribution, e.g.,weighting the propensity numbers for the worst callers to increase theirrespective propensity levels and/or to decrease the respectivepropensity levels of the best callers. The amount of the margin ofweighting is based on the number of callers that are in the queue or inthe grouping at runtime.

Various examples of edge compensation algorithm are now provided.

Notation & Setup

Percentiles have all been divided by 100 and lie between 0 and 1.This experimentation is for a one dimensional unit interval caller toagent matching algorithm, where the goals are to:

-   -   1. Minimize the average difference between agent and caller        percentiles.    -   2. In the absence of explicit interpolation towards performance        routing have a uniform utilization of agents or average wait        time for callers.    -   3. Incorporate a simple algorithm to interpolate between uniform        utilization of agents and performance based routing.        L1=A situation where there are multiple free agents and a single        call arrives and is routed to one of the free agents        L2=A situation where there are multiple calls in the queue and a        single agent becomes available. One of the calls in the queue is        routed to the free agent.        Kappa=In the L1 situation: A method of interpolating between        pure performance routing (when a call comes it is assigned to        the best performing free agent) and a more uniform agent        utilization, An embodiment is the rescaling of agent percentiles        by raising the percentile to the kappa power for some kappa        greater than 1.        Rho=In the L2 situation: A method for when there are of        interpolating between pure performance routing (When an agent        becomes comes available and is assigned to the best call in the        queue. In this case calls with low percentile may have waiting        times much longer than calls with high percentiles) and a more        uniform call expected waiting time algorithm. An embodiment is        the rescaling of agent percentiles by raising the percentile to        the rho power for some rho greater than 1.

The code to experiment was written in R.

T=time in seconds, time is discretemethod=has values now for the current methodology, lah for Look AheadMethod, asm for Adaptive Shift Method, ssm for Static Shift Method, ssm6for Static Shift Method v6, adm for Average Distance Method, and acl forAlternating Circle, for the new methods being analyzednagents=number of agentsaht=average handling timemu=equilibrium average number of free agentsbase_ap=agent percentile before any adjustments or transformations,evenly distributed in [0,1) starting at 1/(2*nagents)base_cp=caller percentile before any adjustments or transformations,uniformly distributed in [0,1)method_ap=vector of agent percentiles after any adjustment ortransformation due to the method applied.method_cp=vector of callers percentiles who are in the queue waiting foragents in the order in which they entered the queue after any adjustmentor transformation due to the method applied.base_ap[freeagents]=the vector of agent percentiles for free agents onlybase_cp[freeagents]=the vector of caller percentiles for call in thequeue

Adaptive Shift Method L1

Method: This is the situation where there are one or more free agentsand at most one call in the queue. At any point in time, shift thepercentiles of the agents by{(1−max(base_ap[freeagents]))−max(base_ap[freeagents])}/2. That is:

asm _(—) ap=base_(—) ap+(1−max(base_(—) ap[freeagents]))/2−min(base_(—)ap[freeagents])/2

Theory: The basis for this method is the observation that there would beno edge effect if there were no edge. That is, if the agent and callerpercentiles were evenly distributed along a circle of length 1. Naivelyapplied, this would result in some awful matches as callers with base_cpclose to 1 might be matched with agents whose base_ap is close to 0. Thecritical observation for avoiding this is that under the naïve adaptiveshift methodology, the probability that the call is assigned to thei^(th) free agent is (asm_ap_(i+1)−asm_ap_(i−1))/2 for an internal(neither the top (n^(th)) nor the bottom (i^(th))) free agent and(1−asm_ap_(n)+asm_ap₂)/2 for the bottom agent and(asm_ap₁+1−asm_ap_(n−i))/2 for the top free agent. If the free agentpercentiles are rigidly shifted at runtime so that the bottom and topedges are the same, then the probabilities are preserved and the zerocall is always matched to the bottom free agent and the 1 call is alwaysmatched to the top free agent. A downside of this method is that someinterior matches will be less than optimal due to the shift.

Kappa: Implementation of Kappa to first order, Kappa>1 results in higherutilization of the top agents and lower utilization of the bottomagents. However, the adaptive shift means that this imbalance inutilization results in a larger edge region allocated to thebottom—which results in a second order increase in utilization of thebottom. Numerical simulation confirms this, resulting in an edgetendency to the mean. Numerous functional forms were numericallyattempted to compensate for the secondary effects. The final functionalform selected was:

ap=base_(—) ap−min(base_(—) ap[freeagents])+(min(base_(—)ap[freeagents])/1)̂(kappa−1)*0.5*(min(base_(—)ap[freeagents])+(1−max(base_(—) ap[freeagents])))

ap=ap̂kappa.

Adaptive Shift Method L2:

Method: This is the situation where there are one or more callers in thequeue and at most one free agent. At any point in time, shift thepercentiles of the call queue by {(1−max(base_cp))−max(base_cp)}/2. Thatis:

asm _(—) cp=base_(—) cp+(1−max(base_(—) cp))/2−max(base_(—) cp)/2.

Theory: The basis for this method is the observation that there would beno edge effect if there were no edge. That is, if the agent and callerpercentiles were evenly distributed along a circle of length 1. Naivelyapplied, this would result in some awful matches as callers with base_cpclose to 1 might be matched with agents whose base_ap is close to 0.When there is a single agent, the adaptive shift method prevents theseawful matches by rotating the callers' percentiles so that the agent isequally likely to be to the left of the caller with the lowest base_cpor to the right of the caller with the highest base_cp. This results inthe same probability that any give caller in the queue is matched aswhen done on the circle. The downside of this method is that someinterior matches will be less than optimal.

Rho: Rho is simple to implement with the formula below and there are nocomplications, although there is a slight decrease in utilization of thetop agents.

asm _(—) cp with rho=asm _(—) cp̂rho.

Note: When there is a single free agent and multiple callers in thequeue, one should not apply a shift to the agent's percentile becausethat shift will move the agent's percentile to 0.5 and automaticallymatch to the median percentile caller. One should leave the agentpercentile unchanged and only shift the callers' percentile. Similarly,when there is a single caller and multiple free agents, one should onlyshift he agents' percentile. When there are multiple free agents andmultiple callers in the queue many possibilities come to mind threeare: 1) no shift until enough callers and agents have been paired sothat there is either one caller or one agent left unmatched and thenrevert to the adjusted shift method; or 2) shift the larger group shiftuntil enough callers and agents have been paired so that there is eitherone caller or one agent left unmatched and then revert to the adjustedshift method; or 3) shift both groups until enough callers and agentshave been paired so that there is either one caller or one agent leftunmatched and then revert to the adjusted shift method. The particularmethod chosen will be dependent on a number of factors, including, butnot limited to: match accuracy; simplicity; abandoned call behavior; andexternal service level agreements (e.g. limiting call wait time orregarding agent utilization).

Note (Theoretical Performance Limits): Because there is interactionbetween the top and bottom of the agent stack, it is clear that thismethod is not the best possible utilization homogenization solution fromthe view of accuracy of match.

Static Shift Method: L1

Method: This is the situation where there are one or more free agentsand at most one call in the queue. At any point in time, rescale thepercentiles of the agents to a length of (1−1/count_freeagents) andshift the percentiles by 1/(2*count_freeagents). That is:

ssm _(—) ap=base_(—) ap*(1-1/count_freeagents)+1/(2*count_freeagents)

Theory: This method is an attempt to address the major weakness of theAdaptive Shift Method. That is—the direct interaction between the topand bottom agents which lowers match performance and makes kappadifficult to implement. We attempt to average out the cumulative effectof the adaptive shifts and perform a static affine transformation. Thetransformation is quite ad hoc. Additional variations are possible.

Kappa: Kappa is simple to implement via the formula below and there areno complications, although there is a slight decrease in utilization ofthe top agents.

ssm _(—) ap with kappa=ssm _(—) ap̂kappa.

Static Shift Method: L2:

Method: The same theory as for the Adaptive Shift Methodology.Theory: The same method as for the Adaptive Shift Methodology.Rho: Rho is simple to implement with the formula below and there are nocomplications, although there is a slight decrease in utilization of thetop agents.

ssm _(—) cp with rho=ssm _(—) cp̂rho.

Static Shift Method: v5 and v6: L1:

Method: This is the situation where there are one or more free agentsand at most one call in the queue. At any point in time, first rescalethe base_ap to start at 0 and end at 1 rather than start at1/(2*nagents) and end at 1−(1/(2*nagents)), second rescale thepercentiles of the agents to a length of (1−1/count_freeagents) andshift the percentiles by 1/(2*count_freeagents). That is:

ssm6_(—) ap=(base_(—)ap−1/(2*nagents))*(1−1/nagents)*(1−1/count_freeagents)+1/(2*count_freeagents).

Theory: This method is an attempt to address the major weakness of theStatic Shift Method. A pattern emerges in the utilization when there arerelatively few free agents. A number of variations were tested—throughv8, but v5 and v6 are listed here. The transformation selected is quitesimple although it is ad hoc. Some patterns remain, although they aremuted.Kappa: For v5, kappa is applied directly.

asm5_(—) ap with kappa=asm5_(—) ap̂kappa

However, it was found that the edge buffers inserted by ssm5 should beadjusted when kappa is applied—otherwise the utilization of the bottomagent is too high and of the top agent is too low. An ad hoc adjustmentthat seemed to word reasonably well is to divide the bottom edge bufferby kappa and add the difference to the top edge buffer. This is what waschosen for ssm6.

asm6_(—) ap with kappa=(asm6_(—)ap−(1/(2*count_freeagents))+(1/(2*count_freeagents))/(kappa))̂kappa.

Static Shift Method: v5 and v6: L2:

Method: The same theory as for the Adaptive Shift Methodology.Theory: The same method as for the Adaptive Shift Methodology.Rho: For v5, rho is applied directly.

asm5_(—) cp with rho=asm5_(—) ap̂rho.

Average Distance Method: L1:

Method: This is the situation where there are one or more free agentsand at most one call in the queue. This method is a variation of theAdaptive Shift and Static Shift Methods that attempts to adjust theaffine shift to the configuration at each point in time. The point ofview is that all gaps should be considered in determining the edgeadjustment—not just the two edge gaps.

avg_distance = 0 for(j in 1:(length(ap_freeagents)−1)) { avg_distance =avg_distance + ((ap_freeagents[j+1]−ap_freeagents[j]){circumflex over( )}2)/2 } avg_distance = avg_distance +((min(ap_freeagents)+(1−max(ap_freeagents))){circumflex over ( )}2)/2 ap= (base_ap −min(ap_freeagents))*(1-2*avg_distance)/(max(ap_freeagents)−min(ap_freeagents)) +avg_distance.Theory: This method is an attempt to address the major weakness of theAdaptive Shift Method. That is—the direct interaction between the topand bottom agents which lowers match performance and makes kappadifficult to implement. This method is a variation of the Adaptive Shiftand Static Shift Methods that attempts to adjust the affine shift to theconfiguration at each point in time. The point of view is that all gapsshould be considered in determining the edge adjustment—not just the twoedge gaps. This method likely could be improved with tweaks.Kappa: For adm, kappa is applied directly.

adm _(—) ap with kappa=adm _(—) ap̂kappa

Average Distance Method: L2:

Method: The same theory as for the Adaptive Shift Methodology.Theory: The same method as for the Adaptive Shift Methodology.Rho: For adm, rho is applied directly via the formula below.

adm _(—) cp with rho=adm _(—) ap̂rho.

Alternating Circle Method: L1:

Method: This is the situation where there are one or more free agentsand at most one call in the queue. At any point in time, map thepercentiles of the free agents to the circle [−1,1) by alternating thesignum. In addition, we randomize the starting signum. First we triedonly varying the signum on the freeagents. That is:

(acl _(—) ap[freeagents])[i]=rbinom(1,1,0.5)*(base_(—)ap[freeagents])[i]*(−1)̂i.

The caller cp is not altered. Distances are computed based onidentifying −1 and +1. Unfortunately, numerical experimentation showed ahigher order masking effect due the reason for which is not clear. Forthis reason we tried a static variation of the signum. That is:

acl _(—) ap[i]=rbinom(1,1,0.5)*acl _(—) ap[i]*(−1)̂i

Again, the caller cp is not altered and distances are computed based onidentifying −1 and +1. This resulted in perfect homogenization ofutilization.Theory: The basis for this method is the observation that there would beno edge effect if there were no edge. In this case we change thetopology so that the agent and caller percentiles are evenly distributedalong a circle of length 2 (take the interval [−1,+1) and identify theendpoints −1 and +1). By randomizing the starting signum for the freeagents, we can leave the caller on the [0,1) portion of the circle. Theshortcoming of this method is that sometimes the caller cp is far fromthe endpoints 0 and 1, but the “best” match is on the negative side ofthe circle and the second “best” match is not very good.Kappa: For acl, kappa is applied directly with the formula below.

acl _(—) ap with kappa=|acl _(—) ap|̂kappa*signum(acl _(—) ap).

Alternating Circle Method: L2:

Method: This is the situation where there are one or more callers in thequeue and at most one free agent. At any point in time, map thepercentiles of the callers in the queue to the circle [−1,+1) byalternating the signum. In addition, we randomize the starting signum.That is:

(acl _(—)cp[callqueue])[i]=rbinom(1,1,0.5)*(base_cap[callqueue])[i]*(−1)̂i

Distances are computed based on identifying −1 and +1.Theory: The basis for this method is the observation that there would beno edge effect if there were no edge. That is, if the agent and callerpercentiles were evenly distributed along a circle of length 2. Byrandomizing the starting signum for the call queue, we can leave theagent on the [0,1) portion of the circle. The shortcoming of this methodis that sometimes the agent ap is far from the endpoints 0 and 1, butthe “best” match is on the negative side of the circle and the second“best” match is not very good.Rho: For acl, rho is applied directly via the formula below.

acl _(—) cp with rho=|acl _(—) cp|̂kappa*signum(acl _(—) cp)

Look Ahead Method: L1:

Method: This is a fairly complex method.

bestagent_now = bestagent  count_freeagents = sum(agents == 0) freeagent_score = rep(0,count_freeagents)  penalty =rep(0,count_freeagents)  discount_factor = 1.0  for(j in1:count_freeagents) { penalty[j] = abs(ap_freeagents[j]−thecall)post_ap_freeagents = ap_freeagents[−j] freeagent_score[j] =(post_ap_freeagents[1]{circumflex over ( )}2)/2 + (1−post_ap_freeagents[count_freeagents−1]{circumflex over ( )}2)/2if(count_freeagents > 2) { for(k in 1:(count_freeagents−2)) {freeagent_score[j] = freeagent_score[j] + ((post_ap_freeagents[k+1]−post_ap_freeagents[k]){circumflex over ( )}2)/4 } } } bestagent2 =which(freeagents)[which.min(penalty + freeagent_score*discount_factor)]if(bestagent[1] != bestagent2[1]){now_ne_lah_counter =now_ne_lah_counter + 1}.Theory: This method is an attempt modify the current greedy method intoone that penalizes the greed to the extent that it makes the nextcaller's expected distance greater.Kappa: For adm, kappa is applied directly.

lah _(—) ap with kappa=lah _(—) ap̂kappa.

Look Ahead Method: L2:

Theory: The same method as for the Adaptive Shift Methodology.Rho: For lah, rho is applied directly via the formula below.

lah _(—) cp with rho=lah _(—) ap̂rho.

Edge Utilization: The graphs for kappa=1, were examined. The results aresummarized here and several of these graphs are included to make thevisualization clear. The first graph illustrates the situation withoutan edge correction.

-   -   For mu=40, acl, and asm agent utilization graphs have the        desired flat profile. The profile for ssm6 is V shaped (however,        only a small portion of the variability is attributable to this        shape—the range of the utilization is 90 for ssm6 and 69/70 for        asm/acl). The agent utilization graph for adm is W shaped. For        now and lah the shape is an upside down U.    -   For mu=20: acl, asm, ssm6 agent utilization graphs have the        desired flat profile, the agent utilization graph for adm is W        shaped. For now and lah the shape is an upside down U.    -   For mu=10, 5, 2: acl, asm, and ssm6 have the desired flat        profile. The rest do not.    -   For mu=0: acl and asm have the desired flat profile. ssm6 has a        bit of a shape to it, but the difference between highest and        lowest agent utilization is only slightly higher than for acl        and asm.

The graphs for kappa=1.2 and 1.4 were examined. The results aresummarized and several graphs are included to make the visualizationclear.

-   -   Acl accommodates kappa seamlessly, with a monotonically        increasing utilization    -   now, asm, ssm5 and ssm6 have utilization that decreases for high        ap. The decrease in utilization for highest ap agents is        greatest for now and least for ssm6. The decrease for ssm6 is        sufficiently small that we believe there is no reason to look        for a better solution at this time.

The method selected for actual implementation depends on a variety offactors. Including:

-   -   Importance of minimizing agent and caller percentile        differences.    -   Agent utilization requirements of call center    -   Service level agreements concerning caller wait times    -   Technological implementation limitations

Higher dimensional analogues occur when additional agent and callerparameters are matched. Other topologies are also possible. Forinstance, the most straight forward extension is for the agent andcaller to each be characterized by two independent parameters, the firstbeing the performance percentile as previously discussed and the secondbeing a percentile based on the expected handle time for the agent andcaller, respectively. The edge compensation algorithm analogues of themethods discussed above would then correspond to insertion of a marginat the edge (additionally widened near the vertices) or alteration ofthe topology from a square to a cylinder or torus). If categoricalparameters are matched, the topology may be disconnected.

Referring to FIG. 12. embodiments of a method are disclosed fordistribution compensation. Block 1200 represents an operation ofobtaining, by one or more computers, agent parameter data for a set ofagents.

Block 1210 represents an operation of ranking or percentiling, by theone or more computers, the agents based at least in part on the agentparameter data, to obtain an agent distribution of agent rankings orpercentiles. In embodiments, agent performance comprises one or moreselected from the group of sale or no sale, number of items sold percall, and revenue per call coupled with handle time

Block 1220 represents an operation of partitioning, by the one or morecomputers, callers in a set of callers based on one or more criteriainto a set of partitions. In embodiments, the partition for the callersis based at least in part on one or more selected from the group ofdemographic data, area code, zip code, NPANXX, VTN, geographic area, 800number, and transfer number.

Block 1230 represents an operation of obtaining, by one or morecomputers, caller propensity data for the respective partitions.

Block 1240 represents an operation of ranking or percentiling, by theone or more computers, the callers based at least in part on datarelating to or predicting a caller propensity for a desired outcomebased at least in part on the caller propensity data of the partitionsof the respective callers, to obtain a caller distribution of callerrankings or percentiles.

Block 1250 represents an operation of performing distributioncompensation, by the one or more computers, using at least one algorithmselected from the group of: an edge compensation algorithm applied to atleast one selected from the group of the distribution of the agentrankings or percentiles and the distribution of the caller rankings orpercentiles, near at least one edge of the respective distribution, toobtain edge compensated rankings or percentiles; and a topology alteringalgorithm applied to either or both of the distribution of the agentrankings or percentiles and the distribution of the caller rankings orpercentiles to change the distributions to a different topology.

In embodiments, the performing the distribution compensation step usesthe edge compensation algorithm and may provide edge compensation onlyto the agent rankings or percentiles. In embodiments, the performing thedistribution compensation step uses the edge compensation algorithm andmay provide edge compensation only to the caller rankings orpercentiles. In embodiments, the performing the distributioncompensation step uses the edge compensation algorithm and may provideedge compensation to both the caller rankings or percentiles and theagent rankings or percentiles.

In embodiments, the distribution compensation step uses the edgecompensation algorithm and may takes the agents that are free atruntime, and rescale the respective agent rankings or percentiles forthese runtime available agents to provide more space/margin at the edgesof the agent distribution. In embodiments, the amount of the margin isbased at least in part on a number of the agents that are free atruntime.

In embodiments, the distribution compensation step uses the edgecompensation algorithm and may takes the callers in a queue or othergrouping at runtime and rescale the respective caller propensityrankings or percentiles, to provide more space/margin at the edges ofthe distribution. In embodiments, the amount of the margin is based atleast in part on a number of callers that are in the queue or in thegrouping at runtime.

In embodiments, the distribution compensation step uses the edgecompensation algorithm and may weight multiple of the agents free atruntime near at least one edge of the agent distribution and weightmultiple of the callers near at least one edge of the callerdistribution to increase utilization of the agents at the at least oneedge of the agent distribution.

In embodiments, the distribution compensation step uses the edgecompensation algorithm and may weight multiple of the agents free atruntime near both edges of the agent distribution or weight multiple ofthe callers near both edges of the caller distribution.

Block 1260 represents an operation of matching, by the one or morecomputers, a respective one of the agents with a respective ranking orpercentile to a respective one of the callers in one of the partitionswith a closest respective ranking or percentile, where at least one ofthe caller ranking or percentile or the agent ranking or percentile hasbeen distribution compensated.

In embodiments, the distribution compensation step uses the topologyaltering algorithm, which algorithm may convert the distribution of theagent performances and the distribution of the caller to a circle.

In embodiments, the distribution compensation step uses the topologyaltering algorithm, which algorithm may convert the distribution of theagent performances and the distribution of the caller to remove theedges of the distribution.

In embodiments where the distribution compensation step uses the edgecompensation algorithm, the Kappa for the distribution of the agents maybe greater than 1.0.

In embodiments, rho applied to the callers in a queue may be 1.0. Notethat rho is the analogue for Kappa as applied to calls. A rho=1.0 is theanalogue of a flat utilization of calls, e.g., the same expected L2 waittime for all call CP's. A rho greater than 1.0 is a setting in whichthere is a bias to answer the highest ranked or percentiled calls first.

Many of the techniques described here may be implemented in hardware orsoftware, or a combination of the two. Preferably, the techniques areimplemented in computer programs executing on programmable computersthat each includes a processor, a storage medium readable by theprocessor (including volatile and nonvolatile memory and/or storageelements), and suitable input and output devices. Program code isapplied to data entered using an input device to perform the functionsdescribed and to generate output information. The output information isapplied to one or more output devices. Moreover, each program ispreferably implemented in a high level procedural or object-orientedprogramming language to communicate with a computer system. However, theprograms can be implemented in assembly or machine language, if desired.In any case, the language may be a compiled or interpreted language.

Each such computer program is preferably stored on a storage medium ordevice (e.g., CD-ROM, hard disk or magnetic diskette) that is readableby a general or special purpose programmable computer for configuringand operating the computer when the storage medium or device is read bythe computer to perform the procedures described. The system also may beimplemented as a computer-readable storage medium, configured with acomputer program, where the storage medium so configured causes acomputer to operate in a specific and predefined manner.

FIG. 10 illustrates a typical computing system 1000 that may be employedto implement processing functionality in embodiments of the invention.Computing systems of this type may be used in clients and servers, forexample. Those skilled in the relevant art will also recognize how toimplement the invention using other computer systems or architectures.Computing system 1000 may represent, for example, a desktop, laptop ornotebook computer, hand-held computing device (PDA, cell phone, palmtop,etc.), mainframe, server, client, or any other type of special orgeneral purpose computing device as may be desirable or appropriate fora given application or environment. Computing system 1000 can includeone or more processors, such as a processor 1004. Processor 1004 can beimplemented using a general or special purpose processing engine suchas, for example, a microprocessor, microcontroller or other controllogic. In this example, processor 1004 is connected to a bus 1002 orother communication medium.

Computing system 1000 can also include a main memory 1008, such asrandom access memory (RAM) or other dynamic memory, for storinginformation and instructions to be executed by processor 1004. Mainmemory 1008 also may be used for storing temporary variables or otherintermediate information during execution of instructions to be executedby processor 1004. Computing system 1000 may likewise include a readonly memory (“ROM”) or other static storage device coupled to bus 1002for storing static information and instructions for processor 1004.

The computing system 1000 may also include information storage system1010, which may include, for example, a media drive 1012 and a removablestorage interface 1020. The media drive 1012 may include a drive orother mechanism to support fixed or removable storage media, such as ahard disk drive, a floppy disk drive, a magnetic tape drive, an opticaldisk drive, a CD or DVD drive (R or RW), or other removable or fixedmedia drive. Storage media 1018 may include, for example, a hard disk,floppy disk, magnetic tape, optical disk, CD or DVD, or other fixed orremovable medium that is read by and written to by media drive 1012. Asthese examples illustrate, the storage media 1018 may include acomputer-readable storage medium having stored therein particularcomputer software or data.

In alternative embodiments, information storage system 1010 may includeother similar components for allowing computer programs or otherinstructions or data to be loaded into computing system 1000. Suchcomponents may include, for example, a removable storage unit 1022 andan interface 1020, such as a program cartridge and cartridge interface,a removable memory (for example, a flash memory or other removablememory module) and memory slot, and other removable storage units 1022and interfaces 1020 that allow software and data to be transferred fromthe removable storage unit 1018 to computing system 1000.

Computing system 1000 can also include a communications interface 1024.Communications interface 1024 can be used to allow software and data tobe transferred between computing system 1000 and external devices.Examples of communications interface 1024 can include a modem, a networkinterface (such as an Ethernet or other NIC card), a communications port(such as for example, a USB port), a PCMCIA slot and card, etc. Softwareand data transferred via communications interface 1024 are in the formof signals which can be electronic, electromagnetic, optical or othersignals capable of being received by communications interface 1024.These signals are provided to communications interface 1024 via achannel 1028. This channel 1028 may carry signals and may be implementedusing a wireless medium, wire or cable, fiber optics, or othercommunications medium. Some examples of a channel include a phone line,a cellular phone link, an RF link, a network interface, a local or widearea network, and other communications channels.

In this document, the terms “computer program product,”“computer-readable medium” and the like may be used generally to referto physical, tangible media such as, for example, memory 1008, storagemedia 1018, or storage unit 1022. These and other forms ofcomputer-readable media may be involved in storing one or moreinstructions for use by processor 1004, to cause the processor toperform specified operations. Such instructions, generally referred toas “computer program code” (which may be grouped in the form of computerprograms or other groupings), when executed, enable the computing system1000 to perform features or functions of embodiments of the presentinvention. Note that the code may directly cause the processor toperform specified operations, be compiled to do so, and/or be combinedwith other software, hardware, and/or firmware elements (e.g., librariesfor performing standard functions) to do so.

In an embodiment where the elements are implemented using software, thesoftware may be stored in a computer-readable medium and loaded intocomputing system 1000 using, for example, removable storage media 1018,drive 1012 or communications interface 1024. The control logic (in thisexample, software instructions or computer program code), when executedby the processor 1004, causes the processor 1004 to perform thefunctions of the invention as described herein.

It will be appreciated that, for clarity purposes, the above descriptionhas described embodiments of the invention with reference to differentfunctional units and processors. However, it will be apparent that anysuitable distribution of functionality between different functionalunits, processors or domains may be used without detracting from theinvention. For example, functionality illustrated to be performed byseparate processors or controllers may be performed by the sameprocessor or controller. Hence, references to specific functional unitsare only to be seen as references to suitable means for providing thedescribed functionality, rather than indicative of a strict logical orphysical structure or organization.

The above-described embodiments of the present invention are merelymeant to be illustrative and not limiting. Various changes andmodifications may be made without departing from the invention in itsbroader aspects. The appended claims encompass such changes andmodifications within the spirit and scope of the invention.

We claim:
 1. A method, comprising: obtaining, by one or more computers, agent performance data for a set of agents; ranking or percentiling, by the one or more computers, the agents based at least in part on the agent performance data; dividing, by the one or more computers, the agents into agent performance ranges based at least in part on the ranking or percentiling step; partitioning, by the one or more computers, callers in a set of callers based on one or more criteria into a set of partitions; determining for each of the partitions, by the one or more computers, an outcome value comprising a statistical chance or likelihood for a particular outcome at least for a first one of the agent performance ranges and for a second one of the agent performance ranges based at least in part on caller partition data; calculating, by the one or more computers, for each of the partitions an outcome value difference indicator based at least in part on the outcome value for the first agent performance range and the outcome value for the second agent performance range; performing distribution compensation, by the one or more computers, using an edge compensation algorithm applied to at least one selected from the group of a distribution of the agent rankings or percentiling and a distribution of caller rankings or percentiling, near at least one edge of the respective distribution, to obtain edge compensated rankings or percentiles, wherein the ranking or percentile of the callers is based at least in part on the outcome values for the partitions of the respective callers; and matching, by the one or more computers, a respective agent with respective performance data to a respective caller in one of the partitions, based at least in part on the outcome value difference indicators for the partitions, where at least one of the agent rankings or percentiles or caller rankings or percentiles has been distribution compensated.
 2. The method as defined in claim 1, wherein the performing the distribution compensation step uses the edge compensation algorithm to provide edge compensation only to the agent rankings or percentiles.
 3. The method as defined in claim 1, wherein the performing the distribution compensation step uses the edge compensation algorithm to provide edge compensation only to the caller rankings or percentiles.
 4. The method as defined in claim 1, wherein the performing the distribution compensation step uses the edge compensation algorithm to provide edge compensation to both the caller rankings or percentiles and the agent rankings or percentiles.
 5. The method as defined in claim 1, wherein the distribution compensation step uses the edge compensation algorithm to take the agents that are free at runtime, and to rescale the respective agent rankings or percentiles for these runtime available agents to provide more space/margin at the edges of the agent distribution.
 6. The method as defined in claim 5, wherein the amount of the margin is based at least in part on a number of the agents that are free at runtime.
 7. The method as defined in claim 1, wherein the distribution compensation step uses the edge compensation algorithm to weight multiple of the agents logged in at runtime near at least one edge of the agent distribution and to weight multiple of the callers near at least one edge of the caller distribution to increase utilization of the agents at the at least one edge of the agent distribution.
 8. The method as defined in claim 1, wherein the distribution compensation step uses the edge compensation algorithm to weight multiple of the agents free at runtime near both edges of the agent distribution or to weight multiple of the callers near both edges of the caller distribution.
 9. The method as defined in claim 1, wherein the determining an outcome value step comprises determining a mean outcome value for a particular outcome for the agent performance range.
 10. The method as defined in claim 1, wherein the partition is based at least in part on one or more selected from the group of demographic data, area code, zip code, NPANXX, VTN, geographic area, 800 number, and transfer number.
 11. The method as defined in claim 1, wherein the outcome value comprises one or more selected from the group of sale, number of items sold per call, and revenue per call coupled with handle time.
 12. The method as defined in claim 1, wherein the matching step is based at least in part on a rule to assign a higher performing agent to a caller from one of the partitions when the outcome value difference indicator for the partition of the caller is higher relative to the outcome value difference indicators of other of the partitions, and to assign a lower performing agent to a caller from one of the partitions when the outcome value difference indicator for the partition of the caller is lower relative to the outcome value difference indicators of other of the partitions, wherein the higher performing agent and the lower performing agent are determined based at least in part on the agent performance data for the respective agent relative to the agent performance data for other of the agents.
 13. The method as defined in claim 1, wherein the matching step further comprises the steps: calculating, by the one or more computers, a ranking or percentile for the partitions by outcome value difference indicator from a high outcome value difference indicator to a low outcome value difference indicator based at least in part on a first number of callers; calculating a ranking or percentile of the agent performances, by the one or more computers, for the respective agents in the set of agents based at least in part on the respective agent performance data and a second number of callers; and matching, by the one or more computers, the respective agent to the respective caller based at least in part on a rule to minimize a difference between the partition ranking or percentile of the outcome value difference indicator for the partition of the respective caller and the performance ranking or percentile of the respective agent.
 14. The method as defined in claim 1, wherein the matching step is performed based at least in part using a multi-data element pattern matching algorithm in a pair-wise fashion to obtain a score or other indicator for each of multiple caller-agent pairs from a set of agents and a set of callers.
 15. The method as defined in claim 1, further comprising: switching, by the one or more computers, between the outcome value difference indicator matching algorithm and a second matching algorithm that is different from the outcome value difference indicator matching algorithm, based on one or more criteria; and when there has been switching to the second matching algorithm, then performing, by the one or more computers, the second matching algorithm to match a respective one of the agents with a respective one of the callers.
 16. The method as defined in claim 15, further comprising: obtaining results data, by the one or more computers, using each of the outcome value difference indicator matching algorithm and the second matching algorithm in the matching step; determining a switch-over point in a distribution for agent performance and/or a distribution for caller propensity for the particular outcome where better results are obtained from one of the algorithms relative to the other of the algorithms; and using, by the one or more computers, this switch-over point to switch between the algorithms.
 17. The method as defined in claim 1, further comprising: switching, by the one or more computers, between the outcome value difference indicator matching algorithm and a second matching algorithm configured to match a respective agent with a respective ranking or percentile to a caller in one of the partitions with a closest ranking or percentile, based on one or more criteria; and when there has been switching to the second matching algorithm, then performing, by the one or more computers, the second matching algorithm to match a respective one of the agents with a respective one of the callers.
 18. A non-transitory computer-readable medium comprising computer-readable codes to be executed by one or more computers, the computer-readable code comprising: computer-readable code to obtain, by one or more computers, agent performance data for a set of agents; computer-readable code to rank or percentile, by the one or more computers, the agents based at least in part on the agent performance data; computer-readable code to divide, by the one or more computers, the agents into agent performance ranges based at least in part on the ranking step; computer-readable code to partition, by the one or more computers, callers in a set of callers based on one or more criteria into a set of partitions; computer-readable code to determine for each of the partitions, by the one or more computers, an outcome value comprising a statistical chance or likelihood for a particular outcome at least for a first one of the agent performance ranges and for a second one of the agent performance ranges based at least in part on caller partition data; computer-readable code to calculate, by the one or more computers, for each of the partitions an outcome value difference indicator based at least in part on the outcome value for the first agent performance range and the outcome value for the second agent performance range; computer-readable code to perform distribution compensation, by the one or more computers, using an edge compensation algorithm applied to at least one selected from the group of a distribution of the agent rankings or percentiles and a distribution of caller rankings or percentiles, near at least one edge of the respective distribution, to obtain edge compensated rankings, wherein the ranking or percentiles of the callers are based at least in part on the outcome values for the partitions of the respective callers; and computer-readable code to match, by the one or more computers, a respective agent with respective performance data to a respective caller in one of the partitions, based at least in part on the outcome value difference indicators for the partitions, where at least one of the agent rankings or percentiles or caller rankings or percentiles has been distribution compensated.
 19. A call center system, comprising: one or more computers configured with program code to perform the following method: obtaining, by the one or more computers, agent performance data for a set of agents; ranking or percentiling, by the one or more computers, the agents based at least in part on the agent performance data; dividing, by the one or more computers, the agents into agent performance ranges based at least in part on the ranking or percentiling step; partitioning, by the one or more computers, callers in a set of callers based on one or more criteria into a set of partitions; determining for each of the partitions, by the one or more computers, an outcome value comprising a statistical chance or likelihood for a particular outcome at least for a first one of the agent performance ranges and for a second one of the agent performance ranges based at least in part on caller partition data; calculating, by the one or more computers, for each of the partitions an outcome value difference indicator based at least in part on the outcome value for the first agent performance range and the outcome value for the second agent performance range; performing distribution compensation, by the one or more computers, using an edge compensation algorithm applied to at least one selected from the group of a distribution of the agent rankings or percentiling and a distribution of caller rankings or percentiling, near at least one edge of the respective distribution, to obtain edge compensated rankings or percentiles, wherein the ranking or percentile of the callers is based at least in part on the outcome value for the partitions of the respective callers; and matching, by the one or more computers, a respective agent with respective performance data to a respective caller in one of the partitions, based at least in part on the outcome value difference indicators for the partitions, where at least one of the agent rankings or percentiles or caller rankings or percentiles has been distribution compensated.
 20. The system as defined in claim 19, wherein the performing the distribution compensation step uses the edge compensation algorithm to provide edge compensation only to the agent rankings or percentiles.
 21. The system as defined in claim 19, wherein the performing the distribution compensation step uses the edge compensation algorithm to provide edge compensation only to the caller rankings or percentiles.
 22. The system as defined in claim 19, wherein the distribution compensation step uses the edge compensation algorithm to take the agents that are free at runtime, and to rescale the respective agent rankings or percentiles for these runtime available agents to provide more space/margin at the edges of the agent distribution.
 23. The system as defined in claim 22, wherein the amount of the margin is based at least in part on a number of the agents that are free at runtime.
 24. The system as defined in claim 19, wherein the distribution compensation step uses the edge compensation algorithm to weight multiple of the agents logged in at runtime near at least one edge of the agent distribution and to weight multiple of the callers near at least one edge of the caller distribution to increase utilization of the agents at the at least one edge of the agent distribution.
 25. The system as defined in claim 19, wherein the distribution compensation step uses the edge compensation algorithm to weight multiple of the agents free at runtime near both edges of the agent distribution or to weight multiple of the callers near both edges of the caller distribution.
 26. The system as defined in claim 19, wherein the determining an outcome value step comprises determining a mean outcome value for a particular outcome for the agent performance range.
 27. The system as defined in claim 19, wherein the partition is based at least in part on one or more selected from the group of demographic data, area code, zip code, NPANXX, VTN, geographic area, 800 number, and transfer number.
 28. The system as defined in claim 19, wherein the matching step is based at least in part on a rule to assign a higher performing agent to a caller from one of the partitions when the outcome value difference indicator for the partition of the caller is higher relative to the outcome value difference indicators of other of the partitions, and to assign a lower performing agent to a caller from one of the partitions when the outcome value difference indicator for the partition of the caller is lower relative to the outcome value difference indicators of other of the partitions, wherein the higher performing agent and the lower performing agent are determined based at least in part on the agent performance data for the respective agent relative to the agent performance data for other of the agents.
 29. The system as defined in claim 19, wherein the matching step further comprises the steps: calculating, by the one or more computers, a ranking or percentile for the partitions by outcome value difference indicator from a high outcome value difference indicator to a low outcome value difference indicator based at least in part on a first number of callers; calculating a ranking or percentile of the agent performances, by the one or more computers, for the respective agents in the set of agents based at least in part on the respective agent performance data and a second number of callers; and matching, by the one or more computers, the respective agent to the respective caller based at least in part on a rule to minimize a difference between the partition ranking or percentile of the outcome value difference indicator for the partition of the respective caller and the performance ranking or percentile of the respective agent.
 30. The system as defined in claim 19, further comprising: switching, by the one or more computers, between the outcome value difference indicator matching algorithm and a second matching algorithm that is different from the outcome value difference indicator matching algorithm, based on one or more criteria; and when there has been switching to the second matching algorithm, then performing, by the one or more computers, the second matching algorithm to match a respective one of the agents with a respective one of the callers.
 31. The system as defined in claim 30, further comprising: obtaining results data, by the one or more computers, using each of the outcome value difference indicator matching algorithm and the second matching algorithm in the matching step; determining a switch-over point in a distribution for agent performance and/or a distribution for caller propensity for the particular outcome where better results are obtained from one of the algorithms relative to the other of the algorithms; and using, by the one or more computers, this switch-over point to switch between the algorithms.
 32. The system as defined in claim 19, further comprising: switching, by the one or more computers, between the outcome value difference indicator matching algorithm and a second matching algorithm configured to match a respective agent with a respective ranking or percentile to a caller in one of the partitions with a closest ranking or percentile, based on one or more criteria; and when there has been switching to the second matching algorithm, then performing, by the one or more computers, the second matching algorithm to match a respective one of the agents with a respective one of the callers. 